Library

[Slon]

Python Tkinter Masterclass - Learn Python GUI Programming
h264, yuv420p, 1280x720 |ENGLISH, aac, 48000 Hz, 2 channels s16 | 13h 23 mn | 6.3 GB
Instructors: Tim Buchalka's Learn Programming Academy, Volkan Atış

Build Python Tkinter Desktop Applications
What you'll learn

The fundamentals of Python GUI programming.
Understand how to create your own Python GUI applications with the Tkinter GUI Library.
Learn how to use advanced Python functions
Learn to Build commercial Desktop Applications with Python Tkinter
Get the most out of the sqlite database for Python projects

Requirements

A basic knowledge of Python is recommended - A good pre-requisite would be the Python Masterclass available on this channel
A computer running Windows or Linux, or a Mac so that the free IDE can be downloaded and install (full instructions included).

Description

Have you done some Python programming but want to take your skills to the next level? Maybe you just want to add GUI programming to your box of tricks.

Or take your Python scripts to a new level by introducing a graphical user interface to your boring console applications.

I am sure if you are reading this you already know that Python rocks. It's got some serious horsepower and putting applications together takes less code compared to traditional languages like Java and C#.

By not having the skills to build GUI apps, you are seriously limiting your potential.

This course is designed to help remove that limitation.

If you are looking to learn Python GUI programming, quickly and efficiently then you are in the right place.

Volkan Atış, your instructor is a very experienced developer, and knows Python GUI application development inside out. And is now here to pass on those skills to you.

Whats covered in the course?

Python GUI Programming using TkInter (which is Pythons default de facto standard for building GUI Apps).

Not only will you learn how to use Python to build Desktop applications, but Volkan will take you on a journey where you will also explore advanced GUI programming, creating actual Python applications, using Sqlite advanced database queries and loads more!

Why is this course different?

This is a comprehensive course on TkInter - no details about the Tkinter Module & GUI Programming is skipped.

You will build real life projects to see TkInter in action in a realistic scenario.

What you will learn in this course?

You will learn how to build real GUI Python programs using TkInter and by the end of the course will be a super productive and skilled Python TkInter programmer.

Volkan Atış, your instructor is an expert Python developer so you can be assured you are learning from a true professional and learning the real-world skills you need to know to succeed.

If you are ready to get started, click on that enroll button and start taking your Python GUI skills to new levels
Who this course is for:

If you are a beginner programmer, then this course is probably not for you. In that scenario consider taking the Python Masterclass from this channel.
Programmers who have a reasonable understanding of Python, and are ready to take their skills to new levels.
Existing Python programmers who want to build GUI applications using TkInter.

Homepage

Code:

http://anonymz.com/?https://www.udemy.com/course/python-tkinter-masterclass/

Code:

https://nitroflare.com/view/E4C2312592E631E/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part1.rar
https://nitroflare.com/view/FF4A7862C5728C5/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part2.rar
https://nitroflare.com/view/31F63AC92662F25/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part3.rar
https://nitroflare.com/view/6481CA36AC7E28B/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part4.rar
https://nitroflare.com/view/AF83426174878C9/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part5.rar
https://nitroflare.com/view/98D5EAAEED8F0DB/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part6.rar
https://nitroflare.com/view/A547DF9294BA829/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part7.rar

Code:

https://rapidgator.net/file/6481ba4771ad8d0254535944312019ba/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part1.rar.html
https://rapidgator.net/file/b43856155cfba7f09abfe3d3d537f0c8/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part2.rar.html
https://rapidgator.net/file/bb2c1f8efde4c59ba3bd5396b83becaf/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part3.rar.html
https://rapidgator.net/file/31f5154d11ff5fcd2f237185a9fb53c6/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part4.rar.html
https://rapidgator.net/file/6361b54f791237ff08f47bd28beab6cf/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part5.rar.html
https://rapidgator.net/file/12268c48a58855c4358f842e810ff44c/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part6.rar.html
https://rapidgator.net/file/fd8f9b9edcce67f4e9e35eca18f4479d/Python_Tkinter_Masterclass_-_Learn_Python_GUI_Programming.part7.rar.html

[Slon]

SAP Inventory Management
Created by Uplatz Training | Last updated 11/2019
Duration: 9.5 hours | Video: h264, 1280x720 | Audio: AAC, 44 KHz, 2 Ch | 5.2 GB
Genre: eLearning | Language: English + Sub | 20 lectures


What you'll learn

In-depth knowledge of Inventory Management

What is SAP Inventory Management

Understand the Inventory Management process in SAP MM

Stock Types and Movement Types

Process of Goods Receipt

Process of Goods Issue

Stock Transfers

Return to Vendor

Physical Inventory

Configuration of Inventory Management in SAP MM

Become an efficient Inventory Manager

Become an SAP MM Consultant

Learn end-to-end process of SAP Inventory Management

Requirements

Enthusiasm and determination to make your mark on the world!

Description

Inventory management is a systematic methodology to obtain, store, manage and optimize the raw materials and finished goods in an organization. It deals with the management of stock, either on value or quantity basis, and includes planning, entry, and keeping records of all the movements of goods.
SAP Inventory Management software helps to sense changes in demand, supply, and network alignment for inventory optimization. SAP inventory software provides a number of tools to simplify the management of raw materials, finished goods and any intermediate inventories or goods.
Organizational efficiency is the goal of SAP inventory management with focus on following processes:
Monitoring data-Gaining knowledge of inventory levels and change in levels over time
Interpreting data-Generating action items based on the data
Executing action items-Assigning the action items to your team
\n
Course Curriculum - Inventory Management in SAP
Basics of Inventory Management
Stock Types
Movement Types
Process of Goods Receipt
Process of Goods Issue
Stock Transfers\ni. What is Stock Transfer\nii. Types of Stock Transfer\niii. Transfer between Plants\niv. Transfer between Storage Locations\nv. Transfer between Stock types\nvi. Transfer using Stock Transport Order
Return to Vendor\ni. Conditions under material returned to vendor\nii. Prerequisites for return to vendor
Cancellation
Initial Stock
Reservations
Physical Inventory
Configuration
i.   Plant Parameters
ii.  Number Range assignment
iii. Screen Selection
iv. End User Restrictions for Inventory Management activities
v.  Movement Types and its attributes
vi. Individual transaction configuration

Who this course is for:

SAP MM/WM Consultants / Managers / End Users
Anyone aspiring to become SAP MM/WM Consultant / Manager
SAP Inventory Controllers
SAP Material Controllers
SAP Processors
SAP Logistics Analysts & Consultants
SAP MM/Supply Chain Inventory Management Analysts
Warehouse Operations Managers
SAP & ERP Professionals
SAP Functional Consultants
Business Analysts & Consultants
Project Managers & Supervisors
Anyone looking forward to start SAP career

Code:

https://nitroflare.com/view/F1B3E04D0C904A5/SAP_Inventory_Management.part1.rar
https://nitroflare.com/view/28C0EA73E488E32/SAP_Inventory_Management.part2.rar
https://nitroflare.com/view/89BA0A8B10DBFEE/SAP_Inventory_Management.part3.rar
https://nitroflare.com/view/74138A72BDF1E11/SAP_Inventory_Management.part4.rar
https://nitroflare.com/view/477A7F9CEA72A5E/SAP_Inventory_Management.part5.rar
https://nitroflare.com/view/753E657AE1D6679/SAP_Inventory_Management.part6.rar

Code:

https://rapidgator.net/file/6cc56c7c68bec0cbd4332367f504f785/SAP_Inventory_Management.part1.rar.html
https://rapidgator.net/file/6cad6bfccd52c5b30e16047227152cfe/SAP_Inventory_Management.part2.rar.html
https://rapidgator.net/file/8389442f2ece34dc9bab88e132a889a0/SAP_Inventory_Management.part3.rar.html
https://rapidgator.net/file/f3dec692f1966784cb10407aef309c44/SAP_Inventory_Management.part4.rar.html
https://rapidgator.net/file/ee544dcb78f8b63c6c76fbd0bc6b9c23/SAP_Inventory_Management.part5.rar.html
https://rapidgator.net/file/355599da3b06339eda2c18f561fae6be/SAP_Inventory_Management.part6.rar.html

[Slon]

How to launch an awesome online lifestyle business
Genre: eLearning | MP4 | Video: h264, 1920x1080 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 5.74 GB | Duration: 11 hours


What you'll learn
Have a solid foundation to start and run a profitable online business
Learn how a $500K/year online business is run from the inside
Learn proven strategies to grow revenues, marketing, sales, hiring, accounting, and project managing

Requirements
Laptop and Internet connection
Patience, perseverance, entrepreneurial spirit and lots of passion!
Description
If you are thinking to start your own online business I'm sure you will find this course extremely helpful.

My name is Miguel Hernandez, for the last 6 years I've been making a living exclusively from the Internet.

In 2010 I started an online animation studio called Grumo Media and have been very fortunate to work with hundreds of clients world wide, including big Fortune 500 companies like Microsoft and Walmart, to even hollywood stars like Ashton Kutcher.

I also make money by selling courses online like this one through my sites and on platforms like Udemy where I have now over 20,000 paid students.

I've learned so much in these years by talking to other successful online business owners, by reading countless books and articles, by continuously building systems to optimize my online businesses and of course by making lots of mistakes along the way.

I really enjoy teaching online and find extremely rewarding when I hear from students that have learned valuable skills through my courses.

I have courses on animation, website development, course creation, and even online dating.

Now I've settled on creating a very comprehensive course to help people create and run a successful small businesses online.

Any business, whether online or offline is made of a large number of moving parts that need to work well together for the business to grow, be profitable and last.

For a first time business owner, even to someone with experience, learning how to build and orchestrate all these parts can be truly overwhelming.

In this course I want to help you learn about all those parts and build systems around them to simplify and in some cases even automate a lot of the tasks required to keep them all working nicely together.

The good thing is that all businesses have many parts in common. All businesses need a product or service that provides value to a specific audience. They all need a way to attract customers through marketing, to convert prospects into buyers through sales, to hire talent, to keep their finances in order through accounting, and they all need systems to manage all these parts, to track metrics, traffic, leads, sales, conversions, followers, to manage communications, etc.

My goal in this course is to provide you with a solid understanding of how to run your own online business by sharing all the tricks, tools, and advice I've collected over the years.

If you are already running an online business I think you will love to have an insight into the systems I've developed, get some ideas, and apply them to help your own business.

I don't intend to be one of those "gurus" that promise "secrets" to making easy money online.

It pains me to see how many people claim to be able to provide these fool-proof one-button turn key systems that for only $49 a month they will provide you with the life style of a rockstar.

This course is a down to earth account of what it takes to run a small online business. Let me tell you right now, making money online in a honest way it's not easy and it takes a lot of work and patience.

It surely beats many 9-5 boring corporate jobs and if you are lucky and work hard and smart you'll be able to both quit your dreaded job and work from home or anywhere in the world (with Internet) like I've been doing for so many years.

There are many types of businesses you can create online. I'm sure anyone running any kind of online business will find parts of this course valuable. However, in this course I'm going to concentrate on the type of business I have experience with which is mainly a service type business with some products on the side.

Grumo Media is an online digital media agency. We specialize in producing promotional videos or explainer videos for companies of all sizes. Our videos cost an average of $10K and in some cases we've produced videos as costly as $50K.

I believe this course will be very useful for anyone getting started today with zero revenues to online businesses generating up to 1 million dollars a year in revenue. (I'm focusing on that revenue bracket because the structure of most businesses change quite a bit after revenues are over a million a year. A 1MM+ business needs to hire people dedicated to each department, rent a space, maybe have a legal team. It gets more complex and it stops being a life style business to become a bigger corporation with all the headaches that entails.)

I've really enjoyed building a business that supports me and my family and provides me with the necessary income and free time to enjoy a balanced and happy life style. I want to give you an in depth look a what a business like that looks from the inside.

This course is not for you (although you may still enjoy watching it like someone that watches Netflix for fun)

if your goal is to raise tons of money, build a startup with the goal to exit and sell the company in 5 years. This course is like a blueprint of a fun small bootstrapped life style business that will be able to support a medium size family anywhere in the world.

If that sounds like you then I'd love to have you on board for this journey.

You will learn about marketing, sales, client relations, hiring, accounting, social media, business systems, contracts, and much much more.

If you are a freelancer, a consultant, a speaker, or even a mom stuck at home, this will be like going to business college from the comfort of your home, like getting an MBA on how to have fun and make a comfortable living online doing what you love.

It won't be easy but I think you will enjoy the learning experience regardless of how successful you are.

I remember when the Internet started to become popular around 1996. Just fresh of the airplane as a new landed immigrant in Vancouver, Canada. I always wanted to be able to leverage this wonderful technology called the world wide web and be able to live on my own terms, without bosses, without commuting.

Honestly, it took a lot longer to get to where I wanted. I built my first website back in 2001. It took me another 9 years of tinkering online before I could quit my job and live 100% of my Internet income.

I know, I was slow compared to many self made Internet multimillionaires. I wanted to be Mark Zuckerberg but I was not smart enough to got to Harvard or ambitious enough to pursue building a multi billion empire.

I just wanted to enjoy life, to have time to spend travelling, going back to Spain every year, playing soccer with my friends. A simple life without huge responsibilities or liabilities.

I'm no Tim Ferris or Gary Vaynerchuck either, no Seth Godin or Paul Graham. People like them have been an inspiration and my indirect mentors. I'm just Miguel Hernandez from Bilbao, Spain and very slowly I carved my own niche in the vast new world of zeros and ones.

I've learned a lot in this time and I think now is a good time to share some of the lessons so when you build your own successful business you can speed up through the learning process.

In the end, I'm creating the course I wish it existed 10 years ago when I decided I wanted to make a living online.

Let's do it!

Who this course is for:
Driven people looking to start an ethical online business
Professionals looking for an alternate source of income online
Not for people looking to get rich quickly without effort

Homepage

Code:

https://anonymz.com/?https://www.udemy.com/course/how-to-build-an-awesome-online-lifestyle-business/

Code:

https://nitroflare.com/view/CBF404FBB118533/How_to_launch_an_awesome_online_lifestyle_business.part1.rar
https://nitroflare.com/view/F2C409C3689D265/How_to_launch_an_awesome_online_lifestyle_business.part2.rar
https://nitroflare.com/view/01FB50F3E361477/How_to_launch_an_awesome_online_lifestyle_business.part3.rar
https://nitroflare.com/view/8A263BB65074393/How_to_launch_an_awesome_online_lifestyle_business.part4.rar
https://nitroflare.com/view/8412596169EAC61/How_to_launch_an_awesome_online_lifestyle_business.part5.rar
https://nitroflare.com/view/CDBBD4F5B7115CC/How_to_launch_an_awesome_online_lifestyle_business.part6.rar

Code:

https://rapidgator.net/file/71d93441591051f7ce1e5599c0478744/How_to_launch_an_awesome_online_lifestyle_business.part1.rar.html
https://rapidgator.net/file/7f367f3f121373d6b1f06629214cb682/How_to_launch_an_awesome_online_lifestyle_business.part2.rar.html
https://rapidgator.net/file/7e462c0956b5dea6cc671e299ec5bea4/How_to_launch_an_awesome_online_lifestyle_business.part3.rar.html
https://rapidgator.net/file/ba95bbdda86733a454c8d398ad97cfd8/How_to_launch_an_awesome_online_lifestyle_business.part4.rar.html
https://rapidgator.net/file/ea10eddae351d1e0ee07c2c595e1ae9c/How_to_launch_an_awesome_online_lifestyle_business.part5.rar.html
https://rapidgator.net/file/7dde733587988451b86fe7e599f858e1/How_to_launch_an_awesome_online_lifestyle_business.part6.rar.html

[Slon]

David Snyder - Killer Influence 2019
Duration: 22.07.48 | Video: h264, 854x480 | Audio: AAC, 44100 Hz, 2 Ch | 27.61 GB
Genre: eLearning | Language: English + Sub

Experience the single MOST POWERFUL covert influence techniques known today through the Killer Influence course. Learn the secrets of covert hypnotic influence and discover how you can use these techniques to create a tremendous amount of influence in your personal and professional life. Instantly allow yourself to heal, grow, and make all of your dreams happen. The Killer Influence system will expand on the lessons learned in the Conversational Persuasion & Influence course, and give you even more awareness and control over your internal emotional states - and what that means for exerting influence in the world.
Once you are ready, Killer Influence will teach you...

How To Create DEEP And Powerful Connections With People
How To Control Your State At Will
How To Connect With Another Human Beings Nervous System,
How To Use That Connection To Send Feelings To Them
Have Them Be More Open To Your Influence
How To Manage Their Emotional State From Across The Room
How To Tap In To The Secret Processes They Use To Make Decisions
How To Automatically Target The Emotional Triggers And Hot Buttons That Make Them Do EXACTLY What You Want Them To Do
How To Be A Master Hypnotist
Learn The Hidden Secrets Of Social Hypnotic Operators
Learn How to Cold Read Any Human
Learn The Hidden Secrets Of Proxemic Hypnotic Operators
Learn The Most Powerful Language Patterns On The Planet
How To Influence Attraction
Learn The Four Pillars Of Hypnotic Influence
Learn Exactly How To Get People To Comply To You

Homepage

Code:

https://www.anonymz.com/?https://www.nlppower.com/product/killer-influence/

Code:

https://nitroflare.com/view/C1B95C9A6F5752A/Killer_influence_2019.part01.rar
https://nitroflare.com/view/ED8F0839294BA57/Killer_influence_2019.part02.rar
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https://nitroflare.com/view/A2A489F2BEA74AC/Killer_influence_2019.part04.rar
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https://nitroflare.com/view/7C804EBCD644D8C/Killer_influence_2019.part27.rar
https://nitroflare.com/view/382352E2435F5C2/Killer_influence_2019.part28.rar

Code:

https://rapidgator.net/file/923ecb0cc993ca1f6007bf196116bc53/Killer_influence_2019.part01.rar.html
https://rapidgator.net/file/8a3aceff09a103f0424bdacf0ff39648/Killer_influence_2019.part02.rar.html
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https://rapidgator.net/file/cb24ef33e50ea217eefd5968028aa1e3/Killer_influence_2019.part10.rar.html
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https://rapidgator.net/file/e5e2e91b26b0a181624452788cc2115a/Killer_influence_2019.part28.rar.html

[Slon]

Green Screen Techniques for Video and Photography (Released 12/2019)
MP4 | Video: AVC, 1280x720 30 fps | Audio: AAC, 48 KHz, 2 Ch | Duration: 2h 49m
Skill Level: Beginner | Genre: eLearning | Language: English + Subtitles | Size: 5.49 GB

Learning to shoot green screen removes many time and budget limitations. Instead of scouting and shooting on location-a prohibitive expense for most productions-you can digitally recreate the background of your scene. This course takes you through the steps of shooting both photography and video on green screen. Authors Rich Harrington and Abba Shapiro walk you through the process of preparing a live action set, from hanging the backdrop to lighting the subject with strobes; positioning and posing your subject; and completing the scene with post-production techniques in Adobe After Effects, Photoshop, and Premiere Pro. Learn how to composite your footage against a new digital background plate, match the color and exposure, adjust focus, and perform keying for motion.This course was created by RHED Pixel. We are pleased to offer this training in our library.

Topics include:

Why use green screen?
Using different backdrops
Lighting the green screen
Lighting the subject
Positioning the subject
Posing techniques
Shooting video with a DSLR or mirrorless camera
Selecting a background
Processing plates and background images
Creating photographic composites
Creating video composites

Code:

https://nitroflare.com/view/38EB8E4CB029F31/Green_Screen_Techniques_for_Video_and_Photography.part1.rar
https://nitroflare.com/view/501075019EBC21C/Green_Screen_Techniques_for_Video_and_Photography.part2.rar
https://nitroflare.com/view/ADE98D3F5B7F286/Green_Screen_Techniques_for_Video_and_Photography.part3.rar
https://nitroflare.com/view/0C3B6AB074E70AD/Green_Screen_Techniques_for_Video_and_Photography.part4.rar
https://nitroflare.com/view/97439B9A274DCDE/Green_Screen_Techniques_for_Video_and_Photography.part5.rar
https://nitroflare.com/view/A44BB8FF383A4C0/Green_Screen_Techniques_for_Video_and_Photography.part6.rar

Code:

https://rapidgator.net/file/e9487fd124ae97f708b0389037d047be/Green_Screen_Techniques_for_Video_and_Photography.part1.rar.html
https://rapidgator.net/file/6474f64d3640cd4dc1782513b733b03e/Green_Screen_Techniques_for_Video_and_Photography.part2.rar.html
https://rapidgator.net/file/9074c81f7bc648d5874d5c18bab2cd18/Green_Screen_Techniques_for_Video_and_Photography.part3.rar.html
https://rapidgator.net/file/e25a46d2e262ba8e53fde27d65ca9a72/Green_Screen_Techniques_for_Video_and_Photography.part4.rar.html
https://rapidgator.net/file/d3c1f3da81d61f7927258e77d7675af7/Green_Screen_Techniques_for_Video_and_Photography.part5.rar.html
https://rapidgator.net/file/dd296bed56d590a1cdb62180ffbb1eab/Green_Screen_Techniques_for_Video_and_Photography.part6.rar.html

[Slon]

MasterClass - Misty Copeland Teaches Ballet Technique and Artistry
Video: .MKV, AVC, 960x540, 24 fps | Audio: English, AAC, 48 KHz, 2 Ch | Duration: 1h 46m | 1.37 GB

From the moment she stepped on the dance floor at age 13, Misty Copeland broke away from the norm. Three months later, the burgeoning ballerina was dancing en pointe. In just over a year, she was performing professionally, a nearly unfathomable accomplishment in the world of classical dance.

Music and performing gave her a voice and helped her rise out of difficult circumstances. They also helped her create a platform to advocate for diversity and inclusion. Now, as the prestigious American Ballet Theatre's first African American female principal dancer, she's an inspiration around the world and a cultural icon. Through her MasterClass, the history-making ballerina is teaching you to develop a deeper appreciation for ballet and the liberating power of movement.

In Misty's class, learn to embrace your own uniqueness and tell a story on stage through the universal language of dance. At the barre, Misty demonstrates her warm-up routine and buildable techniques, from pliés to ronds de jambe, and teaches you to focus on the quality of your moves over repetition. She also breaks down her Black Swan variation and Nutcracker performances and shares her approach to delivering powerful, dynamic performances. She'll show you how she works with partners and with choreographers to create artistry on stage, how she embodies characters, and how you can bring emotion to life through authentic, organic dance and movement.

In this online class, you'll learn about:

• Ballet positions
• Barre warm-ups and techniques
• Ballet moves, from pliés to ronds de jambe
• Diversity and inclusion in ballet
• Misty Copeland's Black Swan variation and Nutcracker performances
• Balancing technique and artistry
• Stage presence
• Delivering dynamic performances
• Embodying characters
• Working with partners
• Collaborating with choreographers

Homepage

Code:

http://anonymz.com/?https://www.masterclass.com/classes/misty-copeland-teaches-ballet-technique-and-artistry

Code:

https://nitroflare.com/view/4F42009DCE661B8/MasterClass_-_Misty_Copeland_Teaches_Ballet_Technique_and_Artistry.part1.rar
https://nitroflare.com/view/3FF56BAF052BEF3/MasterClass_-_Misty_Copeland_Teaches_Ballet_Technique_and_Artistry.part2.rar

Code:

https://rapidgator.net/file/ee45329c341c196a6fa46eecaf90b653/MasterClass_-_Misty_Copeland_Teaches_Ballet_Technique_and_Artistry.part1.rar.html
https://rapidgator.net/file/4c4b28ab45c9264c872ef0b3e60ced21/MasterClass_-_Misty_Copeland_Teaches_Ballet_Technique_and_Artistry.part2.rar.html

[Slon]

The Art of Reading
AVI | Video: 640x480 | Audio: MP3, 48Khz , 2ch | Duration: 12 hours | Language: English | 4.4 GB

You definitely know how to read. But do you know how to read artfully? Unlike everyday reading, artful reading-the way we read novels and short stories-is less about reading for specific information and more about reading to revel in the literary experience.

Full Description

It involves recognizing

how a story's particular narrative style affects your connection with its characters,
why authors choose to hint at meanings instead of just writing them out for you,
how the organization of a novel into distinct chapters can affect your engagement with its plot, and much more.
When you approach a work of fiction the way you do an e-mail or report or newspaper, you miss out on all of this. You're not getting everything you should out of the reading experience. Learning the skills and techniques of artful reading can improve your life in many ways.

If you're a fiction reader, they can make your first reading of a new novel or short story feel as rewarding as a second or third reading, and they can give you new perspectives on works you already cherish.
If you're an aspiring writer, they can help you understand the methods that great writers use to tackle literary concepts-successful methods you can then apply to your own writing.
If you're a book club member, they can enliven discussions and provide your group with engaging activities to create even deeper appreciations of the works you're reading.
If you're a student, they can improve and enhance the close reading skills essential to success in high college, college, and beyond.
And the best part: These skills are not difficult or unwieldy; rather, they are well within your reach. According to award-winning Professor Timothy Spurgin, who has made a career of enlightening students about the benefits of artful reading, great readers are made, not born.

This idea forms the core of The Art of Reading, Professor Spurgin's entertaining 24-lecture course that brings together concepts and techniques rarely found in a single package. Teaching with an engaging and conversational style, he gives you the knowledge and methods to approach even the most daunting reading experience with increased confidence.

Master the Fundamentals of Fiction

An artful reading experience relies on a concrete grasp of the basic elements of fiction, and The Art of Reading is a great way to master them. Throughout the first half of the course, you learn the definitions and characteristics of terms such as authorship, master plot, theme, genre, and metafiction.

While some of these nuts-and-bolts concepts may be familiar to you, Professor Spurgin examines them from multiple angles, revealing hidden meanings that can escape even experienced readers. For example:

How many types of realism are there?
What are the differences between a work's plot and its story?
How can you spot ambiguity in a passage and not confuse it with irony?
Professor Spurgin's answers to these and other hazy questions about the fundamentals of fiction are easily understandable and never bogged down in complicated literary theory. In some instances, he emphasizes a particular element's purposes, strengths, and weaknesses through exercises in which you mentally "rewrite" passages by iconic writers. One intriguing exercise asks you how Edgar Allan Poe's "The Tell-Tale Heart" would read if it were narrated in the third person instead of the first person.

Discover the Artful Reader's Toolbox

Central to Professor Spurgin's lectures are the practical tips and techniques designed to maximize your effectiveness as an artful reader. The Art of Reading gives you a veritable toolbox that you'll find essential to mining everything you need from a novel or short story.

Here is a sample of Professor Spurgin's great suggestions for more artful reading:

Holding an initial reading session: Getting into a book is like getting acquainted with another person, so it's important to make your first reading session a fairly long one-between one hour and 90 minutes. This will give you enough time to become familiar with the author's writing style and the characters. Even if you can't return to the book for days, when you do you'll still be returning to something familiar.
"Pre-reading": Instead of diving headfirst into a new work, leaf through it and explore its organization and structure. Are there chapters, parts, volumes? What might these divisions say about the possible direction of the work? This technique will help make even the lengthiest novel seem less daunting.
Constantly asking questions: Make a point to ask yourself questions about what you're reading, such as the motivations of its characters or the potential outcomes of an event. If you keep brief notes about various possibilities as you continue reading, you'll feel more deeply involved with the characters and their stories.
And these are just a few suggestions! You'll also learn insights into how to contribute to book club discussions, choose the right translation, notice the "beats" in a particular scene, decipher what characters aren't saying in their dialogue, and more.

Learn through Literary "Case Studies"

Throughout the lectures, Professor Spurgin uses a host of literary "case studies" to refine and elaborate on the concepts of artful reading. Unlike other literature-themed courses, The Art of Reading focuses less on a literary analysis of works like A Christmas Carol, Jane Eyre, and The Age of Innocence and more on how artful readers can use their skills to recognize why these works are so significant.

Professor Spurgin also uses literary examples to show how you can finally approach works that, in the past, might have seemed intimidating. He shows you how to read and understand Modernist literature (As I Lay Dying), epic novels (War and Peace), and even the differences between reading a novel and a short story.

In today's busy world, it can be difficult to set aside quality time to savor a great work of literature-the kind of novel or short story that readers have cherished for centuries. After taking this course, you'll be able to use Professor Spurgin's suggested tips to get the most out of the valuable time you spend with these and other classic books.

Rediscover the Joy of Reading

Professor Spurgin understands, first and foremost, the sheer joy of reading and just how contagious that joy is. His engaging teaching skills have brought him numerous teaching honors at Lawrence University-and he delivers every lecture of The Art of Reading in this same acclaimed, award-winning style.

At its core, The Art of Reading is not about complicated literary terms and theories. It's about the wonderful feeling of engaging with a novel or short story on all levels and learning how artful readers think about and approach the works they read. Whether you're someone who loves curling up with a good book, a writer who is looking for insights into how to get inside your readers' minds, or a student who wants to contribute to class discussions, there's something for you to find in this course.

What's more, you won't have to comb through shelves of books searching for ways to get more out of your reading. With The Art of Reading, you'll get a comprehensive and concise package that finally brings together all the myriad ways you can make your future reading experiences more engaging and-most important-more enlightening.

Code:

https://nitroflare.com/view/C21F3FDDCAA3199/The_Art_of_Reading.part1.rar
https://nitroflare.com/view/675823370183031/The_Art_of_Reading.part2.rar
https://nitroflare.com/view/876E748B6E49F79/The_Art_of_Reading.part3.rar
https://nitroflare.com/view/DA01252992AD354/The_Art_of_Reading.part4.rar
https://nitroflare.com/view/DA39D2AD330F38A/The_Art_of_Reading.part5.rar

Code:

https://rapidgator.net/file/6a3a22725a902b7e08ca38f00e8c3183/The_Art_of_Reading.part1.rar.html
https://rapidgator.net/file/437f3939095bfa8e390c464ea05df72f/The_Art_of_Reading.part2.rar.html
https://rapidgator.net/file/53ad9072533886cae2222a3351fce808/The_Art_of_Reading.part3.rar.html
https://rapidgator.net/file/183754b75b7bc6dff6cc7fb81ec7fc3d/The_Art_of_Reading.part4.rar.html
https://rapidgator.net/file/fa59449eb39f7962301315ae8ca88d3d/The_Art_of_Reading.part5.rar.html

[Slon]

Julie Solomon - The Influencer Academy
Duration: 11h28m | Video: h264, 1920x1080 | Audio: AAC, 44100 Hz, s16 | 5.52 GB
Genre: eLearning | Language: English

Let me know if this sounds familiar:You roll over and immediately move your hand until it hits-yep, right there: A hard, shiny glass screen. You tap open your go-to app and pause until the red pop-up displays a few vital metrics: Followers, check. Comments, check. Mentions, check.

And thus begins the day-you'll just add another shiny screen (mornin', laptop!) and cup of coffee to the mix and quite literally work from sunup to sundown . alternating between Bravo and E! in the background + Postmates on the way while the hours tick later into the evening.
Clicking through your Gmail on one display, thumbing through and answering follower comments on another.
Some days it's playing bumper cars in traffic to get to a photoshoot, most days its endless content creation. It's a constant one-thing-after-another reactionary kind of day.
Honestly, it usually feels like one step forward, and five steps back (Okay, let's just be 2-glasses-of-frosé-al-fresco honest: lately it feels like no steps forward-just a dead end on some days).
Things haven't quite worked out like you thought.
Which makes you that much more agitated when family and friends don't see this as a real business.
Your love/hate addiction to scoping out fellow brands used to be a fun inspo sesh-but now, it's a discouraging little reminder of just how much you have on your endless to-do list to turn this blog into a business.
And as new months tick off in your pretty planner, shifting your goals from "well, that month must have not been the right timing" to the next month, even you-the starry-eyed dreamer-are starting to doubt if you've really got the vision and clarity to make this thing work.
If you're going to reach your full potential, cultivate deep trust with your followers, and grow a community that inspires others . instead of constantly feeling like the driver dropped you off on the plateau of brand growth and there's no piece of the "KILLING the game pie" left over for you.
I've invested months of research and in-person time in the trenches with bloggers just like you:
Entrepreneurs with anywhere from 150 to 150K followers, who still struggle to make this online thing their main source of income, no matter how many blogs they post, how many pitches they send out, or how much they max out the room for 30 hashtags per post that Zuckerberg and his team give us.
Entrepreneurs who-even though they've invested in their brand before-have YET to see an online program that's actually helped them cultivate a purpose-driven gameplan and path to their goals.
Entrepreneurs who, despite starting at the SAME time *she* did, consistently sees other bloggers and brands who seem to be achieving momentum and growth. while they feel stagnant.
The result?
An easy-to-follow formula that gives you a crystal-clear blueprint for the way out of overwhelm and discouragement, so you can get confident with the guidance you need to tap into your brilliance as an influencer.
"Because of The Influencer Academy, I figured out how to set up my e-commerce website and already have two products in my online shop."

Homepage

Code:

https://www.anonymz.com/?https://theinfluenceracademy.org/

Code:

https://nitroflare.com/view/E951D6677797D71/The_Influencer_Academy.part1.rar
https://nitroflare.com/view/0F768EA8B538465/The_Influencer_Academy.part2.rar
https://nitroflare.com/view/3FA27414479535C/The_Influencer_Academy.part3.rar

Code:

https://rapidgator.net/file/eef21f06a8a1602cc0c659ee518ddc1b/The_Influencer_Academy.part1.rar.html
https://rapidgator.net/file/4c35267c4de78de1e270bb58af8bf2b1/The_Influencer_Academy.part2.rar.html
https://rapidgator.net/file/0310349d55df5d6ccb9b4eaa3f45030c/The_Influencer_Academy.part3.rar.html

[Slon]

Practical Ethical Hacking - The Complete Course
Practical Ethical Hacking - The Complete Course
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 24h 47m | 11.95 GB
Created by Heath Adams

2020 Launch! Learn how to hack like a pro by a pro. Up to date practical hacking techniques with absolutely no filler.


What you'll learn

Practical ethical hacking and penetration testing skills
Network hacking and defenses
Active Directory exploitation tactics and defenses
Common web application attacks
How to hack wireless networks
Learn how to write a pentest report
Understand the security threats affecting networks and applications
OWASP Top 10
IT security trends

Requirements

Basic IT knowledge
For Mid-Course Capstone: A subscription to hackthebox is suggested, but not required to complete the course.
For Wireless Hacking: A wireless adapter that supports monitor mode (links provided in course).
For Active Directory Lab Build: A minimum of 16GB of RAM is suggested. Students can still participate in the course, but may experience slow lab environments.

Description

Welcome to this course on Practical Ethical Hacking. To enjoy this course, you need nothing but a positive attitude and a desire to learn. No prior knowledge is required.

In this course, you will learn the practical side of ethical hacking. Too many courses teach students tools and concepts that are never used in the real world. In this course, we will focus only on tools and topics that will make you successful as an ethical hacker. The course is incredibly hands on and will cover many foundational topics.

In this course, we will cover:

A Day in the Life on an Ethical Hacker. What does an ethical hacker do on a day to day basis? How much can he or she make? What type of assessments might an ethical hacker perform? These questions and more will be answered.

Effective Notekeeping. An ethical hacker is only as good as the notes he or she keeps. We will discuss the important tools you can use to keep notes and be successful in the course and in the field.

Networking Refresher. This section focuses on the concepts of computer networking. We will discuss common ports and protocols, the OSI model, subnetting, and even walk through a network build with using Cisco CLI.

Introductory Linux. Every good ethical hacker knows their way around Linux. This section will introduce you to the basics of Linux and ramp up into building out Bash scripts to automate tasks as the course develops.

Introductory Python. Most ethical hackers are proficient in a programming language. This section will introduce you to one of the most commonly used languages among ethical hackers, Python. You'll learn the ins and outs of Python 3 and by the end, you'll be building your own port scanner and writing exploits in Python.

Hacking Methodology. This section overviews the five stages of hacking, which we will dive deeper into as the course progresses.

Reconnaissance and Information Gathering. You'll learn how to dig up information on a client using open source intelligence. Better yet, you'll learn how to extract breached credentials from databases to perform credential stuffing attacks, hunt down subdomains during client engagements, and gather information with Burp Suite.

Scanning and Enumeration. One of the most important topics in ethical hacking is the art of enumeration. You'll learn how to hunt down open ports, research for potential vulnerabilities, and learn an assortment of tools needed to perform quality enumeration.

Exploitation Basics. Here, you'll exploit your first machine! We'll learn how to use Metasploit to gain access to machines, how to perform manual exploitation using coding, perform brute force and password spraying attacks, and much more.

Mid-Course Capstone. This section takes everything you have learned so far and challenges you with 10 vulnerable boxes that order in increasing difficulty. You'll learn how an attacker thinks and learn new tools and thought processes along the way. Do you have what it takes?

Exploit Development. This section discusses the topics of buffer overflows. You will manually write your own code to exploit a vulnerable program and dive deep into registers to understand how overflows work. This section includes custom script writing with Python 3.

Active Directory. Did you know that 95% of the Fortune 1000 companies run Active Directory in their environments? Due to this, Active Directory penetration testing is one of the most important topics you should learn and one of the least taught. The Active Directory portion of the course focuses on several topics. You will build out your own Active Directory lab and learn how to exploit it. Attacks include, but are not limited to: LLMNR poisoning, SMB relays, IPv6 DNS takeovers, pass-the-hash/pass-the-password, token impersonation, kerberoasting, GPP attacks, golden ticket attacks, and much more. You'll also learn important tools like mimikatz, Bloodhound, and PowerView. This is not a section to miss!

Post Exploitation. The fourth and fifth stages of ethical hacking are covered here. What do we do once we have exploited a machine? How do we transfer files? How do we pivot? What are the best practices for maintaining access and cleaning up?

Web Application Penetration Testing. In this section, we revisit the art of enumeration and are introduced to several new tools that will make the process easier. You will also learn how to automate these tools utilize Bash scripting. After the enumeration section, the course dives into the OWASP Top 10. We will discuss attacks and defenses for each of the top 10 and perform walkthroughs using a vulnerable web applications. Topics include: SQL Injection, Broken Authentication, Sensitive Data Exposure, XML External Entities (XXE), Broken Access Control, Security Misconfigurations, Cross-Site Scripting (XSS), Insecure Deserialization, Using Components with Known Vulnerabilities, and Insufficient Logging and Monitoring

Wireless Attacks. Here, you will learn how to perform wireless attacks against WPA2 and compromise a wireless network in under 5 minutes.

Legal Documentation and Report Writing. A topic that is hardly ever covered, we will dive into the legal documents you may encounter as a penetration tester, including Statements of Work, Rules of Engagement, Non-Disclosure Agreements, and Master Service Agreements. We will also discuss report writing. You will be provided a sample report as well as walked through a report from an actual client assessment.

Career Advice. The course wraps up with career advice and tips for finding a job in the field.

At the end of this course, you will have a deep understanding of external and internal network penetration testing, wireless penetration testing, and web application penetration testing. All lessons taught are from a real-world experience and what has been encountered on actual engagements in the field.

Note: This course has been created for educational purposes only. All attacks shown were done so with given permission. Please do not attack a host unless you have permission to do so.

Who this course is for:

Beginner students interested in ethical hacking and cybersecurity.

More Info

Code:

https://www.udemy.com/course/practical-ethical-hacking/

Code:

https://nitroflare.com/view/4D15724122865D5/Practical_Ethical_Hacking_-_The_Complete_Course.part01.rar
https://nitroflare.com/view/BDBB445A534CC0B/Practical_Ethical_Hacking_-_The_Complete_Course.part02.rar
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https://nitroflare.com/view/76512F2F5AD4678/Practical_Ethical_Hacking_-_The_Complete_Course.part12.rar

Code:

https://rapidgator.net/file/736625d62daab7dd5dac6a087bbd4bee/Practical_Ethical_Hacking_-_The_Complete_Course.part01.rar.html
https://rapidgator.net/file/253f8aabcd3067a7f89e1c48fa6dc3b3/Practical_Ethical_Hacking_-_The_Complete_Course.part02.rar.html
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https://rapidgator.net/file/9125aa52b3b892f6fd7e1eef4bce55fa/Practical_Ethical_Hacking_-_The_Complete_Course.part06.rar.html
https://rapidgator.net/file/b17714911097a191dcd823bf1314e7e0/Practical_Ethical_Hacking_-_The_Complete_Course.part07.rar.html
https://rapidgator.net/file/d4249128358591ec44a081e93ca563c0/Practical_Ethical_Hacking_-_The_Complete_Course.part08.rar.html
https://rapidgator.net/file/f7c56d46516aa9c895a4f8ca627e2c85/Practical_Ethical_Hacking_-_The_Complete_Course.part09.rar.html
https://rapidgator.net/file/b970d03beeab354e49e89c48f4b6a716/Practical_Ethical_Hacking_-_The_Complete_Course.part10.rar.html
https://rapidgator.net/file/7e393d33565589ed050b5c040e8774cf/Practical_Ethical_Hacking_-_The_Complete_Course.part11.rar.html
https://rapidgator.net/file/7ba4d1686bcf405b747411a5a52a0de3/Practical_Ethical_Hacking_-_The_Complete_Course.part12.rar.html

[Slon]

MasterClass - Neil deGrasse Tyson Teaches Scientific Thinking and Communication
Video: .MKV, AVC, 960x540, 24 fps | Audio: English, AAC, 48 KHz, 2 Ch | Duration: 2h 14m | 1.72 GB

Neil deGrasse Tyson was just nine years old when he became fascinated by the mysteries of the cosmos. Today he's known worldwide for inspiring others to consider the world-and the universe-around us. The astrophysicist, director at the world-renowned Hayden Planetarium in New York City, and science influencer has been a powerful advocate for science literacy with a popular television series and the NYT-bestselling book Astrophysics for People in a Hurry. He's been awarded the U.S. National Academy of Sciences Public Welfare Medal for his "extraordinary role in exciting the public about the wonders of science." Now he's teaching you how he connects with audiences around the world.

In his MasterClass, Neil deGrasse Tyson teaches you how to discover and communicate objective truths in clear, exciting, and engaging ways. Learn to think, measure, and weigh information like a scientist; detect flaws in your own reasoning and navigate cognitive bias; and gauge the credibility of information and ideas. He also teaches you his personal approach to communicating, whether you're presenting to an audience, delivering a sound bite, or simply conversing with friends and family around the dinner table.

In this online class, you'll learn about:

• Scientific literacy
• Cognitive bias
• Personal and political truths
• The scientific method
• Making predictions
• Scientific measurement
• Effective communication
• Connecting with an audience
• Creating a sound bite
• Inspiring curiosity

Homepage

Code:

http://anonymz.com/?https://www.masterclass.com/classes/neil-degrasse-tyson-teaches-scientific-thinking-and-communication

Code:

https://nitroflare.com/view/DB45DA521A4EE11/MasterClass_-_Neil_deGrasse_Tyson_Teaches_Scientific_Thinking_and_Communication.part1.rar
https://nitroflare.com/view/1357552582A2B01/MasterClass_-_Neil_deGrasse_Tyson_Teaches_Scientific_Thinking_and_Communication.part2.rar

Code:

https://rapidgator.net/file/bce6e504e82be0bede42b43307fd0dd7/MasterClass_-_Neil_deGrasse_Tyson_Teaches_Scientific_Thinking_and_Communication.part1.rar.html
https://rapidgator.net/file/41b6ea25902a9a2e97a97e0989f709d0/MasterClass_-_Neil_deGrasse_Tyson_Teaches_Scientific_Thinking_and_Communication.part2.rar.html

[Slon]

MasterClass - David Sedaris Teaches Storytelling and Humor
Video: .MKV, AVC, 960x540, 24 fps | Audio: English, AAC, 48 KHz, 2 Ch | Duration: 3h 24m | 2.62 GB

With regular pieces in The New Yorker, national tours, and appearances on NPR's This American Life, David Sedaris is one of the most recognizable essayists writing today. He's known for his wit, weirdness, satire, and serious humor, and his collections of personal essays-Naked, Me Talk Pretty One Day, Dress Your Family in Corduroy and Denim, When You Are Engulfed in Flames, and Let's Explore Diabetes With Owls-each became immediate bestsellers. He's also the author of a New York Times-bestselling fable collection and has twice had his work included in "The Best American Essays" anthologies.

So far, more than 10 million copies of his books are in print around the world, and he's been nominated for three Grammy Awards for Best Spoken Word and Best Comedy Album. Now the writer Entertainment Weekly calls the "preeminent humorist of his generation" teaches you his tools for writing humor and his approach to the art of personal storytelling.

In David's MasterClass, learn to turn everyday observations into stories with meaning, craft personal essays that resonate from beginning to end, and incorporate humor that not only gets a laugh but also connects with readers on a deeper level. Join David backstage as he workshops and perfects his public readings and find inspiration in his approach to publishing and the writer's life. Find stories and inspiration all around you, hone your humor writing, and embrace the power of your personal perspective.

In this online class, you'll learn about:

• Fine-tuning your observational skills
• Turning observations into stories
• Perfecting your opening line
• Writing with meaning
• Connecting with readers and live audiences
• Writing about family and friends
• Turning vignettes and diary entries into stories
• Tools for writing humor
• Getting the most out of public readings
• Creating and titling collected essays

Homepage

Code:

http://anonymz.com/?https://www.masterclass.com/classes/david-sedaris-teaches-storytelling-and-humor

Code:

https://nitroflare.com/view/7E7F8D01572BA73/MasterClass_-_David_Sedaris_Teaches_Storytelling_and_Humor.part1.rar
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Code:

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https://rapidgator.net/file/3ed20a4bf8446638087145a666ab8155/MasterClass_-_David_Sedaris_Teaches_Storytelling_and_Humor.part3.rar.html

[Slon]

SalesMentor - Traffic and Funnels
Duration: 32.37.26 | Video: h264, 1920x1080 | Audio: AAC, 44100 Hz, s16 | 23.01 GB
Genre: eLearning | Language: English

YOU CAN JOIN THE NEW FRONTIER OF
LEADERSHIP-BASED SALESMANSHIP
And if This New and "Upgraded" Relationship With Us
Does NOT Return At Least 10x for You This Month, It's Free
Sales Mentor Bundle Each Month For 3 Months ( Value: $2,799 )
INCLUDES:
- Exclusive Monthly Internal Training Letter
- Monthly Sales Clinics with my team and Eli Wilde (One of Tony Robbins' top sales guys for 15 years)
- Classic Sales book "Debriefs" to help you cut your learning time
- Monthly Post Call Analysis so you can hear how we do it!
​LIMITED BONUS: (First 50) Our 2-Step Organic Marketing Method ( Value: $199 )
We've never released this training outside our flagship program ClientKit™, and hundreds of clients from that program have already used this exact training to attract 15-20 applicants per week - without paid traffic.

Code:

https://nitroflare.com/view/D094C5314650E32/SalesMentor_by_Traffic_%26_Funnels.part01.rar
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Code:

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[Slon]

Amazon Web Services AWS LiveLessons 2nd Edition
MP4 | Video: AVC 3840 x 2160 | Audio: AAC 48 Khz 2ch | Duration: 17:13:23 | 39.6 GB
Genre: eLearning | Language: English

More Than 17 Hours of Video Instruction

More than 17 hours of video instruction on Amazon Web Services with coverage on cloud computing and available AWS services, as well as a guided hands-on look at using services such as EC2 (Elastic Compute Cloud), S3 (Simple Storage Service), and more.

Overview

Amazon Web Services (AWS) LiveLessons is a unique video product designed to provide a solid foundational understanding of the Amazon Web Services (AWS) infrastructure-as-a-service (IaaS) products. The course covers concepts necessary to understand cloud computing platforms, distributed computing, multi-tier architectures, virtual machines, storage, databases, analytics, high availability, and much more. This course has been designed to show just how simple and cost-effective it is to achieve a superior level of high-availability, fault-tolerance, security, and reduced operational burden in your infrastructure and applications. Detailed throughout the course are a number of use cases designed to spark your imagination and exemplify well-architected solutions within the rich and varied ecosystem that is Amazon Web Services. You can also use this video as a secondary resource to help you study for the AWS Cloud Practitioner and Solutions Architect Exams.

Since the first edition of this course, AWS has added many new services to their offerings, as well as many new features to existing services. The AWS web-based management console has also seen significant updates and improvements. In this second edition, many of those new services and features are covered, and all new diagrams have been provided to more accurately represent those you will see in a real-world scenario. The new edition also includes all new demos to account for changes in the AWS management console user interface.

Amazon Web Services (AWS) LiveLessons contains 14 independent video lessons totaling almost 13 hours of instruction. The videos contain in-depth instruction using live demos, slide instruction, and video captures. Demonstrations of Amazon Web Services and third-party cloud solutions are included to provide necessary context and experience for further study and use of AWS.
Skill Level

Beginner/All levels

Learn How To
Get started with AWS, including networking, computing, storage, managing databases, and security
Understand cloud-computing platforms, and how AWS fits into them
Use EC2, CloudWatch, S3 Buckets, and more
Use IAM, VPC, NACLs, AMI, ECS, EKS, EBS, and other various tools

Who Should Take This Course
Working software developers, system administrators, or solution architects who want to migrate applications to or build applications natively in the cloud
Those pursuing AWS certification.

Course Requirements
None: Beginner level course

Lesson Descriptions

Lesson 1: AWS Overview
In Lesson 1, "AWS Overview," AWS global infrastructure is discussed, and there is a review of some very foundational terms and concepts that apply to everything covered in the remainder of the course. In this lesson, AWS data centers and their locations around the world, as well as a high-level overview of the kinds of services offered, are also covered.

Lesson 2: Identity and Access Management
Lesson 2, "Identity and Access Management," dives into how to secure your accounts and credentials, and ensure proper access to resources. This lesson discusses the Identity and Access Management service, as well as the features and options that can be leveraged to secure access to AWS environments.

Lesson 3: Networking in AWS
In Lesson 3, "Networking in AWS," one of the most important aspects to learn and become proficient at, is discussed in detail. To a seasoned IT professional many things in AWS will be familiar, yet many things will also be new concepts or work in significantly different ways. In this lesson, creating isolated networks with Amazon Virtual Private Cloud, subnets, routing, firewalls, and more are discussed.

Lesson 4: Computing in AWS
Lesson 4, "Computing in AWS," covers the compute options offered by Amazon Elastic Compute Cloud, serverless computing, and container-based applications. It also includes several great demos on these areas.

Lesson 5: Storage in AWS
In Lesson 5, "Storage in AWS," the wide variety of storage options is covered, as well as review demos of creating and using services like Amazon Simple Storage Service and Amazon Elastic Block Store. There are also reviews of backups, snapshots, archiving, and more.

Lesson 6: Databases in AWS
Lesson 6, "Databases in AWS," explores highly available traditional databases on Amazon Relational Database Service, the nosql database Amazon DynamoDB, data warehousing with Amazon Redshift, as well as services for in-memory cache and graph databases.

Lesson 7: High Availability in AWS
In Lesson 7, "High Availability in AWS," Amazon Elastic Load Balancing and Auto Scaling are explored. There is also a discussion of concepts pertinent to achieving a superior degree of high-availability and fault-tolerance.

Lesson 8: Analytics in AWS
Lesson 8, "Analytics in AWS," covers the exciting technologies AWS offers, such as real-time stream processing with Amazon Kinesis, big data processing with Amazon EMR, and Athena. Tools that make it easy to move and transform data between various and destinations are reviewed, as well as a use case highlighting business intelligence by making use of several various services.

Lesson 9: Management Tools
Lesson 9, "Management Tools," reviews monitoring and log collection, user activity tracking, inventory and change tracking, and tools to help build a more secure approach to configurations management.

Lesson 10: Developer Tools
Lesson 10, "Developer Tools," reviews several services related to coding, version control, deployments, and debugging.

Lesson 11: Mobile and Application Services
Lesson 11, "Mobile and Application Services," explores the various tools offered by AWS along with other exciting technologies that ease the development of mobile applications.

Lesson 12: Security in AWS
In Lesson 12, "Security in AWS," controlling access to data, encryption, and other services that can secure our environment through automation are reviewed.

Lesson 13: Cost Management and Billing Options
Lesson 13, "Cost Management and Billing Options," reviews the cost models and highlights often-overlooked cost contributors for various services, as well as tools that can be used to understand and manage those costs.

Lesson 14: Course Wrap Up
Lesson 14, "Course Wrap Up," provides some overarching thoughts about Amazon Web Services, and a few notes about the Solutions Architect exam.

Code:

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Code:

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[Slon]

Ben Oberg - Instagram Mastery 3.0
Language: English | Video: h264, 1920x1080 | Audio: AAC, 44100 Hz, s16 | 37.71 GB
Genre: eLearning

Module 1 - Setting up your account
Module 2 - Content Mastery Part 1 ​
Module 2 - Content Mastery Part 2 ​
Module 3 - Art of the Algo
Module 4 - Ranking Content
Module 5 - Building a brand
Module 6 - DM groups & Powerlikes
Module 7 - IGTV
Module 8 - Instagram Stories
Module 9 - The Interaction Methods
​Module 10 - The Fanpage Strategy
Module 11 - Ads
Module 12 - Influencers/Shoutouts
Module 13 - Social boosting/Exterior Engagement
Module 14 - Increasing Engagement
Module 15 - Growing an account for 0 to 10k LIVE
Module 16 - Top Instagram niches (repost accounts only: not for personal brands, businesses) & Hacks To Grow Each Niche
Module 17 - Paid Ways to Grow
Module 18 - Monetization (Ecom + 9 other ways)
Module 20 - Important tricks & hacks​
Module 21 - Instagram Tools & Apps
Module 22 - New Instagram features

Homepage

Code:

https://www.anonymz.com/?https://benoberg.com/instagram-mastery-3

Code:

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Code:

https://rapidgator.net/file/87be3fdc85f348d62e7984d5cd164250/Instagram_Mastery_3.0.part01.rar.html
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[Slon]

Ultimate Ruby on Rails and Ruby Bundle: Learn Ruby and Rails
Video: .mp4 (1280x720, 30 fps®) | Audio: aac, 44100 Hz, 2ch | Size: 9.7 GB
Genre: eLearning Video | Duration: 134 Lessons (11h 23m) | Language: English

Learn Ruby on Rails and Ruby Programming The Fast and Easy Way!



What you'll learn

Build Web Apps With Ruby On Rails
Build Your Own Stock Market Website
Build Your Own Weather App
Build Your Own To-Do List App
Learn Ruby Programming
Understand Rails Webhosting
Download and Install Ruby on Rails
Download and Install Git Bash Terminal
Push Code To Github
Understand the Bootstrap CSS Framework
Connect To a 3rd Party API
Understand How To Use Databases With Rails


Requirements

You need a computer with Internet access
No programming skills necessary to take this course

Description

This is a bundle course of four of my popular Ruby on Rails and Ruby Programming courses all in one course. Aimed at the absolute beginner, in this course you'll learn the Ruby programming language and the Ruby on Rails Web Framework for building websites.

This course is broken up into four sections:

Ruby Programming

Ruby on Rails To-Do List App

Ruby on Rails Weather App

Ruby on Rails Stock Market App

We'll start out learning the Ruby programming language. You don't need any prior knowledge or special tools to learn Ruby, I'll walk you through everything you need to know. You'll learn:

Printing to the Screen

Variables

Numbers and Simple Math

Comparison Operators

Assignment Operators

After that, we'll move into more intermediate topics like:

Conditional If/Else Statements

While Loops

For Loops

Fizzbuzz

Finally we'll finish up with more advanced topics like:

Arrays

Objects

Functions

Classes

After that we'll learn the Ruby on Rails Web Framework by building out Three awesome Web Apps! I'll teach you how to install Ruby on Rails and set up all the tools you'll need, and then we'll dive right in and start building out apps!

The first app we'll build is a daily "To-Do" List App. It will allows you to create lists of things you want to do in a day (take out the trash, feed the dog, buy groceries, etc). You'll learn how to use a database in Rails, how to add things to the database, and how to remove them.

In the next App, we'll build a Weather App that checks air quality in your neighborhood. We'll learn how to connect to a third party API to grab weather data, and then do cool stuff with it.

In the last app, we'll build a full blown Stock Market Portfolio App. You can lookup stock market data on any company, save the data to your own personal portfolio, and all kinds of other cool things!


If you've every wanted to learn web development with the Ruby on Rails and Ruby stack, this is the course for you.


Sign up today and I'll see you on the inside!


-John Elder


Who this course is for:

This course is meant for absolute beginners who want to learn Ruby on Rails for Web Development
Anyone wanting to learn to build web apps

Code:

https://nitroflare.com/view/65D549F4BD05D34/Ultimate_Ruby_on_Rails_and_Ruby_Bundle_Learn_Ruby_and_Rails.part01.rar
https://nitroflare.com/view/DF42561CA8BC366/Ultimate_Ruby_on_Rails_and_Ruby_Bundle_Learn_Ruby_and_Rails.part02.rar
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https://nitroflare.com/view/14D33DED81801CA/Ultimate_Ruby_on_Rails_and_Ruby_Bundle_Learn_Ruby_and_Rails.part04.rar
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https://nitroflare.com/view/F4A6AA1DBCB001B/Ultimate_Ruby_on_Rails_and_Ruby_Bundle_Learn_Ruby_and_Rails.part10.rar

Code:

https://rapidgator.net/file/1166ca4959fc5c9443d1a1f1a39024c6/Ultimate_Ruby_on_Rails_and_Ruby_Bundle_Learn_Ruby_and_Rails.part01.rar.html
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[Slon]

PyTorch Bootcamp for Artificial Neural Networks and Deep Learning Applications
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 3h 15m | 5.31 GB
Instructor: Minerva Singh

Hands-on PyTorch boot camp for Artificial Intelligence applications with artificial neural networks and deep learning

Key Features

A full introduction to Python Data Science and Anaconda, a powerful Python-driven data science framework
A thorough grounding in how to use PyTorch to implement common deep learning algorithms such as Convolutional Neural Networks (CNNs) on real-life data
Limited mathematical jargon. The course focuses on teaching people basic Python data science concepts and builds up to using PyTorch

What You Will Learn

Deep Learning Basics - Getting started with Anaconda, an important Python data science environment
Neural Network Python Applications - Configuring the Anaconda environment to get started with PyTorch
Introduction to Deep Learning Neural Networks - Theoretical underpinnings of important concepts (such as deep learning) without the jargon
AI Neural Networks - Implementing Artificial Neural Networks (ANNs) with PyTorch
Neural Network Model - Implementing deep learning (DL) models with PyTorch
Deep Learning AI - Implement common machine learning algorithms for image classification
Deep Learning Neural Networks - Implement PyTorch-based deep learning algorithms on image data

About

Master the latest and hottest deep learning frameworks (PyTorch) for Python data science

This course is your complete guide to practical machine learning and deep learning using the PyTorch framework in Python and covers the important aspects of PyTorch. If you take this course, you'll have no need to take other courses or buy books on PyTorch.

In this age of big data, companies across the Globe use Python to sift through the avalanche of information at their disposal; the advent of frameworks such as PyTorch is revolutionizing deep learning.

By gaining proficiency in PyTorch, you can give your company a competitive edge and take your career to the next level.

After taking this course, you'll be able to use packages such as Numpy, Pandas, and PIL to work with real data in Python and you'll be fluent in PyTorch. We even introduce you to deep learning models such as Convolution Neural Networks (CNNs)!

The underlying motivation for the course is to ensure you can apply Python-based data science on real data today, start analyzing data for your own projects whatever your skill level, and impress potential employers with actual examples of your data science abilities.

All the codes and supporting files for this course are available at -

Code:

https://github.com/PacktPublishing/PyTorch-Bootcamp-for-Artificial-Neural-Networks-and-Deep-Learning-Applications

More Info

Code:

https://subscription.packtpub.com/video/programming/9781839218897

Code:

https://nitroflare.com/view/AF5ABD4F3BC3710/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications__Video_.part1.rar
https://nitroflare.com/view/CF7E818B6DEE431/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications__Video_.part2.rar
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https://nitroflare.com/view/B2C757BFF2EAD0D/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications__Video_.part5.rar
https://nitroflare.com/view/D4828B6BDAB1C26/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications__Video_.part6.rar

Code:

https://rapidgator.net/file/08b9846280536e37b8f24ca7eccc5b37/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications_[Video].part1.rar.html
https://rapidgator.net/file/da74629bafbfd9969e190ecb1a827e1f/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications_[Video].part2.rar.html
https://rapidgator.net/file/b3de2a0f48465b171969eeb02ef5a4f2/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications_[Video].part3.rar.html
https://rapidgator.net/file/1a2addeef187e6e798bb8d2b67930c62/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications_[Video].part4.rar.html
https://rapidgator.net/file/d0b223e2fb3ebd3b08b624cddf10976f/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications_[Video].part5.rar.html
https://rapidgator.net/file/445450fcc4f2a0a08c484fbaffca396d/PyTorch_Bootcamp_for_Artificial_Neural_Networks_and_Deep_Learning_Applications_[Video].part6.rar.html

[Slon]

Master Outsourcing | Get the best price & save time
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 48000 Hz
Language: English | VTT | Size: 6.42 GB | Duration: 9.5 hours


What you'll learn
Understand what is and isn't outsourcing
Validate your idea's preparedness and eligibility for outsourcing
Understand how much to budget and ways to reduce project costs
Navigate the biggest outsourcing platforms: UpWork and Freelancer
Use advanced search criteria to find and locate more specific outsourcing partners
Build a contracting strategy that works with your level of experience and your idea's level of progress
Intelligently choose your ideal price point and preferred region
Write job titles, descriptions, and settings that bring in high-quality bids
Use wireframing tools like Balsamiq to quickly put together mockups of your idea
Use advanced techniques to bring your project together faster, negotiate bid prices, and vet potential partners
Employ common and uncommon best practices for outsourcing
Keep your project on track and increase transparency with tools like Asana and Trello
Bug hunt and run QA tests like a professional
Navigate the most common scenarios that arise from outsourcing

Requirements
No pre-requisites.
Familiarity with common online communication tools is helpful but not necessary.
Description
People that are skilled at outsourcing can be 10x more efficient than those who are not. Knowing how to outsource effectively is an incredibly useful skill whether you're an entrepreneur, a manager, a marketer, or small business owner. Being able to incorporate outsourcing into your project, your business, or even your job can dramatically change your rate of success.

Cost is often the single largest factor in whether or not a project succeeds or fails. By mastering outsourcing you can virtually guarantee that you get the lowest and most reasonable price every time you launch or engage in a project.

Learn how to outsource and you'll make magic with your project's to-do list. You'll be able to hit goals sooner while spending a fraction of what you planned. Save your project by going lean and shifting your attention and resources to other crucial areas.

Website development, mobile app development, logo design, market research, customer feedback, email marketing, sales, promotion, scheduling, editing, proofreading, ghostwriting, interviewing, application testing, bookkeeping, employee vetting - these are just some of the things you can outsource.

Virtually every company and startup outsources in some way. Outsourcing has endless possibilities - you just need to know how to use it.

Who this course is for:
Anyone interested in outsourcing.
Anyone interested in getting a detailed understanding of outsourcing and the best ways of contracting work.
Anyone interested in improving their skills with project or product management.

Homepage

Code:

https://anonymz.com/?https://www.udemy.com/course/master-outsourcing-get-the-best-price-save-'

Code:

https://nitroflare.com/view/C9C48E593FE4236/Master_Outsourcing__Get_the_best_price_%26_save_time.part1.rar
https://nitroflare.com/view/F1FA924F35734B8/Master_Outsourcing__Get_the_best_price_%26_save_time.part2.rar
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https://nitroflare.com/view/C35603AA99426E1/Master_Outsourcing__Get_the_best_price_%26_save_time.part6.rar
https://nitroflare.com/view/F3FEBE931B221F0/Master_Outsourcing__Get_the_best_price_%26_save_time.part7.rar

Code:

https://rapidgator.net/file/b4aeda72774e7a4081cc12f3beff03f1/Master_Outsourcing__Get_the_best_price_&_save_time.part1.rar.html
https://rapidgator.net/file/3783674b208d1b82c7d721f377faf9ea/Master_Outsourcing__Get_the_best_price_&_save_time.part2.rar.html
https://rapidgator.net/file/b1383aa8d4afeb14b5dac741461781b5/Master_Outsourcing__Get_the_best_price_&_save_time.part3.rar.html
https://rapidgator.net/file/85efddd2eba77328a951b01cc8c698c9/Master_Outsourcing__Get_the_best_price_&_save_time.part4.rar.html
https://rapidgator.net/file/557d0298056ae3c63eddc8763f249a5c/Master_Outsourcing__Get_the_best_price_&_save_time.part5.rar.html
https://rapidgator.net/file/fd12f8643bf9f7765f18796168f6fe63/Master_Outsourcing__Get_the_best_price_&_save_time.part6.rar.html
https://rapidgator.net/file/9de068bc1ccefe99d30394e200df1081/Master_Outsourcing__Get_the_best_price_&_save_time.part7.rar.html

[Slon]

Learn about Python and Blockchain: The Complete Guide
Created by Mammoth Interactive, John Bura | Video: h264, 1280x720 | Audio: AAC 48KHz 2ch | Duration: 13:13 H/M | Lec: 81 | 5.79 GB | Language: English | Sub: English [Auto-generated]

Blockchain technology explained. Cryptocurrency, cryptography, transactions & programming in the best Blockchain course


What you'll learn

Learn about digital ledgers and transactions like money transfers.
Understand how Blockchains are structured through block mining.
Understand the advantages of Blockchain (such as increased security through cryptography.)
Compare Ethereum vs Bitcoin Blockchain and their cryptocurrency.
Code from scratch in the Solidity programming language.
Build a voting app with data stored on the blockchain.

Requirements
No experience necessary.
Remix, a Solidity IDE. Free from Ethereum.

Description
Create Blockchain projects with the Solidity language. This course was funded by a #1 Kickstarter Project by Mammoth Interactive. Enroll now to:

Download Python and learn with examples

Use machine learning algorithms

Become a blockchain developer

Handle blockchain transactions

Code in Solidity

Build web apps that run on a blockchain

Do you want to build your first decentralized app today? This course is for you.

Build your first decentralized project today. Learn to store data and transactions on the blockchain. Enroll in this ultimate guide and get the competitive edge in any field.

Part 1: Introduction to Blockchains

Define 'blockchain.'

Learn about digital ledgers and transactions like money transfers.

Understand the peer to peer network.

Define 'decentralization.'

Understand how Blockchains are structured through block mining.

And more!

In Part 1 you will understand the advantages of Blockchain (such as increased security through cryptography.) We use practical examples to explain each concept, to help you understand topics like how Blockchain can be used to prevent forgeries.

You will watch slides and animations explaining how blockchains work. Sit back and relax as expert Mammoth Interactive instructor Nimish Narang leads you through step by step.

Part 2: Introduction to Decentralized Apps

Learn how decentralized apps work, and how they're different from regular web apps.

Understand the Blockchain to generate Ether.

Compare Ethereum vs Bitcoin Blockchain and their cryptocurrency.

List the benefits of Ethereum for transactions.

In Part 2 you will understand examples of decentralized apps for the virtual world. You'll learn how D-apps perform data fetch, data addition and data modification. You will learn what determines the cost of a transaction, which is valuable for when we start building a project.

You'll be able to recognize smart contracts. These are similar to classes in object-oriented languages.

Part 3: Solidity Language Basics

Solidity is a fairly rudimentary language that takes bits and pieces from other programming languages. It is easy to use because it's so simple, though it has less functionality. You will become an expert on the basics and key uses that will make you fluent in Solidity.

Declare common variable types of Solidity.

Build real-world examples of arrays.

Use functions to perform practical actions you will see in many projects.

And much more.

You will build arrays for operations, storage, and memory. And much more in this huge Mammoth Interactive course. You will create structs, mappings, messages, and contracts. Each topic will be explained step by step for anyone. No prior coding experience is needed.

Part 4: Build a Decentralized App

Build a simple decentralized voting app.

Implement a smart contract.

Build an app interface using HTML, CSS & JavaScript.

Add unique functions like casting votes and announcing the election winner.

In Part 4 of this Mammoth course, you will build a complete app from zero to hero. You will be able to solve errors that can occur when you work as a programmer. You will build the contract skeleton and code. The result will be a basic app you can build upon and add to your portfolio!

Who this course is for?
Anyone who wants to build decentralized apps.
Absolute beginners with NO experience with blockchains.
Beginners to Solidity who have no experience with decentralized apps.
If you have experience with blockchains, decentralized apps, or Solidity, you may find this course a little slow. You will however get a full review of core topics.

Homepage

Code:

https://www.udemy.com/course/learn-about-python-and-blockchain-the-complete-guide/

Code:

https://nitroflare.com/view/FCDDA9D0EACAD36/Learn_about_Python_and_Blockchain_The_Complete_Guide%21.part1.rar
https://nitroflare.com/view/4D907F7CE3F73CD/Learn_about_Python_and_Blockchain_The_Complete_Guide%21.part2.rar
https://nitroflare.com/view/4D4BBB06800A4DD/Learn_about_Python_and_Blockchain_The_Complete_Guide%21.part3.rar
https://nitroflare.com/view/15A0EDF52DE7605/Learn_about_Python_and_Blockchain_The_Complete_Guide%21.part4.rar
https://nitroflare.com/view/4C5D1788E502C16/Learn_about_Python_and_Blockchain_The_Complete_Guide%21.part5.rar
https://nitroflare.com/view/6F78BFF933A45EB/Learn_about_Python_and_Blockchain_The_Complete_Guide%21.part6.rar

Code:

https://rapidgator.net/file/0701d3707f0e8fb2d13ffd4bc8dce352/Learn_about_Python_and_Blockchain_The_Complete_Guide!.part1.rar.html
https://rapidgator.net/file/583fda9a4815a8dd9fcf2b5e4d4a9c69/Learn_about_Python_and_Blockchain_The_Complete_Guide!.part2.rar.html
https://rapidgator.net/file/a5fa142c15c87e0c37ce763023754a0e/Learn_about_Python_and_Blockchain_The_Complete_Guide!.part3.rar.html
https://rapidgator.net/file/e33b3b6e27457459a0e2e9f01a4e7cb2/Learn_about_Python_and_Blockchain_The_Complete_Guide!.part4.rar.html
https://rapidgator.net/file/a657f5c446b130bee464d4efd9a4c1c8/Learn_about_Python_and_Blockchain_The_Complete_Guide!.part5.rar.html
https://rapidgator.net/file/d75ac95521517306a120de2f03cbae18/Learn_about_Python_and_Blockchain_The_Complete_Guide!.part6.rar.html

[Slon]

Udemy - Learn to Code with Python
Video: .mp4 (1280x720, 30 fps®) | Audio: aac, 44100 Hz, 2ch | Size: 8.88 GB
Genre: eLearning Video | Duration: 30:20:51 | Language: English

Learn and master the world's most popular programming language from A to Z! Complete beginners welcome!


What you'll learn

Master programming in Python, a popular language that powers codebases in tech companies like Instagram, Pinterest, Dropbox and more
Dive into the mechanics of Python's data structures including strings, lists, dictionaries, tuples, sets and more!
Apply your knowledge to solve common interview questions, algorithms and coding challenges
Explore a dynamic curriculum with videos, quizzes, written assignments, in-browser coding challenges and more
Progress from core language fundamentals to advanced features like classes, decorators and unit testing


Requirements

A Windows, MacOS, or Linux computer
An Internet connection
A modern text editor (Visual Studio Code is installed and used in the course)
Persistence and dedication!

Description

Learn to Code with Python is a comprehensive introduction to Python, one of the most widely used programming languages in the world. Python powers codebases in companies like Google, Facebook, Pinterest, Dropbox, and more. Over more than 30 hours of video content, we'll tackle everything you need to know about the language to be an effective developer.

The course is jam-packed with:

30+ hours of video, with new content added frequently

60+ coding challenges that you can complete in your browser

40+ multiple-choice quizzes

35+ written assignments

The course is designed from the ground up to take you from novice to professional. Complete beginners are welcome! Over more than 250+ videos, we'll work our way through everything the language has to offer, from the fundamentals to the advanced features of Python. Topics covered include.

Setup & Installation

Variables

Data types

Functions and Methods

Control Flow

Lists and tuples

Dictionaries

Sets

Modules

Decorators

Classes

Exception Handling

The Python Standard Library

Unit testing

.and more!

Throughout the entire journey, I'll be coding alongside you step by step in the code editor. No boring academic presentations here!

Python holds a special place in my heart - it was the first language I ever learned! I'm honored to be able to pass on years of knowledge to a new group of avid learners. Whether you are a novice who's never written a line of code before or an experienced developer looking to dive into a new language, there's something for you to enjoy in Learn to Code with Python.

I'm super excited to show you what this powerful language can do for you. Thanks for checking out the course!

Who this course is for:

New developers eager to tackle a powerful but simple programming language
Existing programmers looking to learn Python, the world's most popular programming language
Anyone eager to learn the most in-demand skill set of the 21st century!

Code:

https://nitroflare.com/view/F1FD3B3C51CD705/learn-to-code-with-python.part01.rar
https://nitroflare.com/view/CE7BCE6C20D4E76/learn-to-code-with-python.part02.rar
https://nitroflare.com/view/8CC7DA4CDDFB72B/learn-to-code-with-python.part03.rar
https://nitroflare.com/view/9E0485D8BA6160A/learn-to-code-with-python.part04.rar
https://nitroflare.com/view/5FC39ECEB78A130/learn-to-code-with-python.part05.rar
https://nitroflare.com/view/39334173EA41B49/learn-to-code-with-python.part06.rar
https://nitroflare.com/view/469B434CB9AC648/learn-to-code-with-python.part07.rar
https://nitroflare.com/view/B107FF6A37495D2/learn-to-code-with-python.part08.rar
https://nitroflare.com/view/CEB668EF0885361/learn-to-code-with-python.part09.rar

Code:

https://rapidgator.net/file/e28faaf46df7eebd84dc1f48d70eb200/learn-to-code-with-python.part01.rar.html
https://rapidgator.net/file/c2a5f2d7e5e01a191b5c8d9b3613706a/learn-to-code-with-python.part02.rar.html
https://rapidgator.net/file/5d9607bd41af447e227c4747c21b85ff/learn-to-code-with-python.part03.rar.html
https://rapidgator.net/file/f7f456930e96fdde5306114a3f584c13/learn-to-code-with-python.part04.rar.html
https://rapidgator.net/file/dace4b972fd9ceb9574ab82411b92e26/learn-to-code-with-python.part05.rar.html
https://rapidgator.net/file/8a0a21618e8bcc95441e186138691225/learn-to-code-with-python.part06.rar.html
https://rapidgator.net/file/c60deaa0631eb699f531b450859002ed/learn-to-code-with-python.part07.rar.html
https://rapidgator.net/file/e7a760a7f819a23d96c132309f3a5fd6/learn-to-code-with-python.part08.rar.html
https://rapidgator.net/file/8665a2991c30f9825d92aafb78dd9dd2/learn-to-code-with-python.part09.rar.html

[Slon]

Introduction to Machine Learning - 18 Hours
Video: .mp4 (1280x720, 30 fps®) | Audio: aac, 44100 Hz, 2ch | Size: 15.6 GB
Genre: eLearning Video | Duration: 17:54:52 | Language: English

Top University Professor



What you'll learn

Hypothesis Space and Inductive Bias
Evaluation and Cross-Validation
Linear Regression
Learning Decision Tree
Python Exercise on Decision Tree and Linear Regression
and Much MUch More!!


Requirements

Beginners are Welcome!

Description

Hello everyone and welcome to this course on an introduction to machine learning


in this course we will have a quick introduction to machine learning and this will not be very deep in a mathematical sense but it will have some amount of mathematical trigger and what we will be doing in this course is covering different paradigms of machine learning and with special emphasis on classification and regression tasks and also will introduce you to various other machine learning paradigms. In this introductory lecture set of lectures I will give a very quick overview of the different kinds of machine learning paradigms and therefore I call this lectures machine learning. )


A brief introduction with emphasis on brief right, so the rest of the course would be a more elongated introduction to machine learning right. So what is machine learning so I will start off with a canonical definition put out by Tom Mitchell in 97 and so a machine or an agent I deliberately leave the beginning undefined because you could also apply this to non machines like biological agents so an agent is said to learn from experience with respect to some class of tasks right and the performance measure P if the learners performance tasks in the class as measured by P improves with experience. So what we get from this first thing is we have to define learning with respect to a specific class of tasks right it could be answering exams in a particular subject right or it could be diagnosing patients of a specific illness right.


So but we have to be very careful about defining the set of tasks on which we are going to define this learning right, and the second thing we need is of a performance measure P right so in the absence of a performance measure P you would start to make vague statement like oh I think something is happening right that seems to be a change and something learned is there is some learning going on and stuff like that. So if you want to be clearer about measuring whether learning is happening or not you first need to define some kind of performance criteria right.


So for example if you talk about answering questions in an exam your performance criterion could very well be the number of marks that you get or if you talk about diagnosing illness then your performance measure would be the number of patients that you say are the number of patients who did not have adverse reaction to the drugs you gave them there could be variety of ways of defining performance measures depending on what you are looking for right and the third important component here is experience right.


So with experience the performance has to improve right and so what we mean by experience here in the case of writing exams it could be writing more exams right so the more the number of exams you write the better you write it better you get it test taking or it could be a patient's in the case of diagnosing illnesses like the more patients that you look at the better you become at diagnosing illness right.


So these are the three components so you need a class of tasks you need a performance measure and you need some well-defined experience so this kind of learning right where you are learning to improve your performance based on experience is known as a this kind of learning where you are trying to where you learn to improve your performance with experience is known as inductive learning. And then the basis of inductive learning goes back several centuries people have been debating about inductive learning for hundreds of years now and are only more recently we have started to have more quantified mechanisms of learning right. So but one thing I always point out to people is that if you take this definition with a pinch of salt, so for example you could think about the task as fitting your foot comfortably right.


So you could talk about whether a slipper fits your foot comfortably or let me put so I always say that you should take this definition with a pinch of salt because take the example of a slipper you know, so the slipper is supposed to give protection to your foot right and a performance measure for the slipper would be whether it is fitting the leg comfortably or not or whether it is you know as people say there is biting your leg or is it Chaffin your feet right and with experience you know as the slipper knows more and more about your foot as you keep varying the slipper for longer periods of time it becomes better at the task of fitting your foot right as measured by whether it is shattering your foot or whether it is biting your foot or not right.


So would you say that the slipper is learned to fit to your foot well by this definition yes right so we have to take this with a pinch of salt and so not every system that confirms to this definition of learning can be set to learn usually okay. (Refer Slide Time: 06:11) So going on so there are different machine learning paradigms that we will talk about and the first one is supervised learning where you learn an input to output map right so you are given some kind of an input it could be a description of the patient who comes to comes to the clinic and the output that have to produce is whether the patient has a certain disease or not so this they had to learn this kind of an input to output map or the input could be some kind of equation right and then output would be the answer to the question or it could be a true or false question I give you a description of the question you have to give me true or false as the output.


And in supervised learning what you essentially do is on a mapping from this input to the required output right if the output that you are looking for happens to be a categorical output like whether he has a disease or does not have a disease or whether the answer is true or false then the supervised learning problem is called the classification problem right and if the output happens to be a continuous value like, so how long will this product last before it fails right or what is the expected rainfall tomorrow right so those kinds of problems they would be called as regression problems. These are supervised learning problems where the output is a continuous value and these are called as regression problems. So we will look at in more detail classification and regression as we go on right, so the second class of problems are known as unsupervised learning problems right where the goal is not really to produce an output in response to an input but given a set of in data right we have to discover patterns in the data right. So that is more of the testicle unsupervised learning there is no real desired output that we are looking for right we are more interested in finding patterns in the data. So clustering right is one task one unsupervised learning task where you are interested in finding cohesive groups among the input pattern right, for example I might be looking at customers who come to my shop right and I want to figure out if there are categories of customers like so maybe college students could be one category and sewing IT professionals could be another category and so on so forth and when I'm looking at this kinds of grouping in my data, so I would call that a clustering task right.


So the other popular unsupervised learning paradigm is known as the Association rule mining or frequent pattern mining where you are interested in finding a frequent co-occurrence of items right in the data that is given to you so whenever A comes to my shop B also comes to my shop right. So those kinds of co-occurrence so I can always say that okay if I see A then there is likely very likely that B is also in my shop somewhere you know so I can learn these kinds of associations between data right. And again we look at this later in more detail these are I mean there are many different variants on supervised and unsupervised learning but these are the main ones that we look at so the third form of learning which is called reinforcement learning it is neither supervised or unsupervised in nature and typically these are problems where you are learning to control the behavior of a system and I will give you more intuition intone enforcement learning now in one of the later modules, so like I said earlier. (Refer Slide Time: 09:33) So for every task right, so you need to have some kind of a performance measure so if you are looking at classification the performance measure is going to be classification error so typically right.


So we will talk about many, many different performance measures in the duration of this course but the typical performance measure you would want to use this classification error it's how many of the items or how many of the patients did I get incorrect so how many of them who are not having the disease today predict had the disease and how many of them that had the disease that I missed right. So that would be one of the measures that I would use and that would be the measure that we want to use but we will see later that often that is not is not possible to actually learn directly with respect to this measure. So we use other forms right and likewise for regression again so we have the prediction error suppose I say it is going to rain like 23 millimeters and then it ends up raining like 49centimeters I do not know so that is a huge prediction error right and in terms of clustering so this is little becomes a little trickier to define performance measures we don't know what is a good clustering algorithm because we do not know what how to measure the quality of clusters.


So people come up with all different kinds of measures and so one of the more popular ones is a scatter or spread of the cluster that essentially tells you how spread out the points are that belong to a single group if you remember we are supposed to find cohesive groups, so if the group is not that cohesive it's not all of them are not together then you would say the clustering is of a poorer quality and if you have other ways of measuring things like Alec was telling you, so if you know that people are college students right and then you can figure out that how many what fraction of your cluster or college students.




So you can do this kinds of external evaluations so one measure that people use popularly there is known as purity right and in the Association rule mining we use variety of measures called support and confidence that takes a little bit of work to explain support in confidence so I will defer it and I talked about Association rules in detail and in more in the reinforcement learning tasks so if we remember I told you it is learning to control so you are going to have a cost for controlling the system and also the measure here is cost and you would like to minimize the cost that you are going to accrue while controlling the system. So these are the basic machine learning tasks. (Refer Slide Time: 12:11) So there are several challenges when you are trying to build a build a machine learning solution right so a few of these I have listed on this slide right the first one is you have to think about how good is a model that you have learned right so I talked about a few measures on the previous slide but often those are not sufficient there are other practical considerations that come into play and we will look at some of these towards thee there was a middle of the course somewhere right and the bulk of the time would be spent on answering the second question which is how do I choose a model right. So given some kind of data which will be the experience that we are talking about so given this experience how would I choose how would I choose a model right that somehow learns what I want to do right so how that improves itself with experience and so on so how do I choose this model and how do I actually find the parameters of the model that gives me the right answer right. So this is what we will spend much of our time on in this course and then there are a whole bunch of other things that you really have to answer to be able to build a useful machine loose full data analytics or data mining solutions questions like do I have enough data do I have enough experience to say that my model is good right it's the data efficient quality that could be errors in the data right suppose I have medical data and a is recorded as 225, so what does that mean it could be 225 days in which case it is a reasonable number it could be 22.5 years again is a reasonable number or 22.5 months is reasonable.


But if it is 225 years it's not a reasonable number so there is something wrong in the data right so how do you handle these things or noise in images right or missing values so I will talk briefly about handling missing values later in the course but this is as I mentioned in the beginning is a machine learning course right and this is not there is not primarily it is primarily concerned about the algorithms of machine learning and the and the math and the intuition behind those and not necessarily about the questions of building a practical systems based on this. So I will be talking about many of these issues during the course but just that I want to reiterate that will not be the focus right and so the next challenge I have listed here is how confident can I be of the results and I want that I certainly we will talk a little bit because the whole premise of reporting machine learning results depends on how confident you can be of the results right and the last question am I describing the data correctly.


So that is a very, very domain dependent and the question that you can answer only with your experience as a machine learning or a data scientist professional or with time right, so but there are typical questions that you would like to ask that are there on the slides so from the next in the next module we look at the different learning paradigms in slightly more detail.

If you remember in supervised learning we talked about experience right where you have some kind of a description of the data. So in this case let us assume that I have a customer database and I am describing that by two attributes here, age and income.


So I have each customer that comes to my shop I know the age of the customer and the income level of the customers right. (Refer Slide Time: 00:48) And my goal is to predict whether the customer will buy a computer or not buy a computer right. So I have this kind of labeled data that is given to me for building a classifier right, remember we talked about classification where the output is a discrete value in this case it is yes or no, yes this is the person will buy a computer, no the person will not buy a computer.


And the way I describe the input is through a set of attributes in this case we are looking at age and income as the attributes that describe the customer right. And so now the goal is to come up with a function right, come up with a mapping that will take the age and income as the input and it will give you an output that says the person will buy the computer or not buy the computer. So there are many different ways in which you can create this function and given that we are actually looking at a geometric interpretation of the data, I am looking at data as points in space. (Refer Slide Time: 01:57) The one of the most natural ways of thinking about defining this function is by drawing lines or curves on the input space right.


So here is one possible example, so here I have drawn a line and everything to the left of the line right. So these are points that are red right, so everything to the left of the line would be classified as will not buy a computer, everything to the right of the line where the predominantly the data points are blue will be classified as will buy a computer. So how would the function look like, it will look like something like if the income of a person remember that the x-axis is income and the y-axis is age.


So in this case it basically says that if the income of the person is less than some value right, less than some X then the person will not buy a computer. If the income is greater than X the person will buy your computer. So that is the kind of a simple function that we will define. It will just notice that way we completely ignore one of the variables here which is the age. So we are just going by income, if the income is less than some X then the person will not buy a computer, if the income is greater than X the person will buy a computer. So is this a good rule more or less I mean we get most of the points correct right except a few right.


So it looks like yeah, we can we can survive with this rule right. So this is not too bad right, but then you can do slightly better. (Refer Slide Time: 03:29) All right, so now we got those two red points that those just keep that points are on the wrong side of the line earlier. Now seem to be on the right side right, so everything to the left of this line will not buy a computer, everything to the right will buy a computer right, everyone moves to the right will buy a computer. So if you think about what has happened here, so we have improved our performance measure right. So the cost of something, so what is the cost here. So earlier we are only paying attention to the income right, but now we have to pay attention to the age as well right. So the older you are right, so the income threshold at which we will buy a computer is higher right. So the younger you are, younger means lower on the y axis, so the younger you are the income threshold at which you will buy a computer is lower right. So is that clear, so the older you are right, so the income threshold is shifted to the right here right so the older you are, so you need to have a higher income before you buy a computer and the anger you are your income threshold is lower, so you do not mind buying a computer even if your income is slightly lesser right.


So now we have to start paying attention to the age right, but then the advantage is you get much better performance right can you do better than this yes okay. (Refer Slide Time: 04:54) Now almost everything is correct except that one pesky red point, but everything else is correct. And so what has happened here we get much better performance, but at the cost of having a more complex classifier right. So earlier if you thought about it in geometric terms, so first you had a line that was parallel to the y-axis therefore, I just needed to define a intercept on the x-axis right. So if X is less than some value then it was one class was greater than some value was another class.



Then the second function it was actually a slighting line like that, so I needed to define both the intercept and the slope right. And now here it is now a quadratic so I have to define three parameters right. So I have to define something like ax2+ bx+c, so I have defined the ABC the three parameters in order to find the quadratic, and I am getting better performance. So can you do better than this. (Refer Slide Time: 05:57) Okay the sum for does not seem right correct seems to be too complex a function just to be getting this one point there right. And I am not sure I am not even sure how many parameters you need for drawing that because Microsoft use some kind of spline PowerPoint use some kind of spline interpolation to draw this curve I am pretty sure that it is lot, lot more parameters than it is worth another thing to note here is that that particular red point that you see is actually surrounded by a sea of blue right. So it is quite likely that there was some glitch there either the person actually bought a computer and we never we have not recorded it has been having what computer or there are some extremist reason the person comes into the shop sure that is going to buy a computer but then gets a phone call saying that some emergency please come out immediately and therefore he left without buying a computer right there could be variety of reasons for why that noise occurred and this will probably be the more appropriate classifier right. So these are the kinds of issues I would like to think about what is the complexity of the classifier that I would like to have right and versus the accuracy of the classifier, so how good is the classifier in actually recovering the right input output map and or their noise data in the in the input in the experience that I am getting is it clean or is there noise on it and if so how do I handle that noise these are the kinds of issues that we have to look at okay. (Refer Slide Time: 07:31) So these kinds of lines that we drew right kind of hiding one assumption that we are making so the thing is the data that comes to me comes as discrete points in the space right and from these discrete points in the space I need to generalize and be able to say something about the entire state space right so I do not care where the data point is on the x and y-axis right I should be able to give a label to that right. If I do not have some kind of assumption about these lines right and if you do not have some kind of assumptions about these lines the only thing I can do is if the same customer comes again hey or somebody who has exact same age and income as that cause customer comes again I can tell you whether the person is going to buy a computer or not buy a computer but I will not be able to tell you about anything else outside of the experience right. So the assumption we made is everything to the left of a line is going to do one thing or the other right so everything to the left of the line will not buy the computer everything to the right or everyone to the right will buy a computer this is an assumption I made the assumption was the Lions are able to segregate people who buy from who do not buy the lines or the curves were able to segregate people who will buy from who will not buy so that is a kind of an assumption I made about the distribution of the input data and the class labels. So this kind of assumptions that we make about these lines are known as inductive biases in general inductive bias has like two different categories one is called language bias which is essentially the type of lines that I am going to draw my gonna draw straight lines or am I going to draw curves and what order polynomials am I going to look at and so on so forth these for my language bias and such bias is the other form of inductive bias that tells me how in what order am I going to examine all these possible lines right



. So that gives me the gives me a search bias right, so putting these two these things together we are able to generalize from a few training points to the entire space of inputs right I will make this more formal as we go on and then in the next night set of modules right. (Refer Slide Time: 10:01) And so here is one way of looking at the whole process so I am going to be giving you a set of data which we will call the training set so the training set will be will consists of say as an input which we'll call as X and an output which we call as Y right, so I am going to have a set of inputs I have X1, X2, X3, X4 likewise I will have Y1, Y2, Y3, Y4 and this data is fed into a training this data is fed into a training algorithm right and so the data is going to look like this in our case right. So remember our X's are the input variable success all the inputs so in this case that should have the income and the age, so x1 is like 30,000 and 25 and x2 is like 80,000 and 45 and so on so forth and the Y's or the labels they correspond to the colors in the previous picture right so y1 does not buy a computer Y2 buys a computer and so on so forth so this essentially gives me the color coding so y1 is essentially red and y2 is blue right and I really if I am going to use something numeric this is what we will be doing later on I really cannot be using these values first of all wise or not numeric and the X is varied too much right. So the first coordinate in the X is like 30,000 and 80,000 and so on so forth and the second coordinate is like 25 and 45 so that is a lot a lot smaller in magnitude so this will lead to some kind of numerical instabilities, so what will typically end up doing is normalizing these so that they form appropriate approximately in the same range so you can see that I have try to normalize these X values between 0 and 1 right.



So have chosen an income level of say 2 lakhs it is the maximum and age of 100 and you can see the normalized values and likewise for buys and not buy I have taken not by as - 1 and by as computer is + 1these are arbitrary choices, now but later on you will see that there are specific reasons for wanting to choose this encoding in this way alright and then the training algorithm chugs over this data right and it will produce a classifier so now this classifier I do not know I do not know whether it is good or bad right so we had a straight line in the first case right an axis parallel line if we did not know the good or bad and we needed to have some mechanism by which we evaluate this right. So how do we do the evaluation typically is that you have what is called a test set or a validation set right so this is another set of x and y paths like we had in the training set, so again in the test set we know what the labels are it is just that we are not showing it to the training algorithm we know what the labels are because we need to use the correct labels to evaluate whether your trading algorithm is doing good or bad right so, so this process by which this evaluation happens is called validation later then of the validation. If you are happy with the quality of the classifier we can keep it if you are not happy they go back to the training algorithm and say hey I am not happy with what you produced give me something different right, so we have to either iterate over the algorithm again we will go over the data again and try to refine the parameter estimation or we could even think of changing some parameter values and then trying to redo the training algorithm all over again but this is the general process and we will see that many of the different algorithms that we look, look at in the course of fitting the course of these lectures actually follow this kind of a process okay so what happens inside that green box. (Refer Slide Time: 13:48) So inside the training algorithm is that there will be this learning agent right which will take an input and it will produce an output white at which it thinks is the correct output right but it will compare it against the actual target why it was given for the in the training right, so in the training you actually have a target why so it will compare it against a target why right and then figure out what the error is and use the error to change the agent right so then it can produce the right output next time around this is essentially an iterative process so you see that input okay produce an output Y hat and then you take the target Y. You can compare it to the Y hat figure out what is the error and use the error to change the agent again right and this is by and large the way most of the learning all algorithms will operate most of the classification algorithms or even regression algorithms will open it and we will see how each of this works as, we go on right there are many, many applications. (Refer Slide Time: 14:46) I mean this is too numerous to list here are a few examples you could look at say a fraud detection right, so we have

some data where the input is a set of transactions made by a user and then you can flag each transaction as a valid transaction or not you could look at sentiment analysis you know varied Lee called opinion mining or buzz analysis etc. Where I give you a piece of text or a review written about and a product or a movie and then you tell me whether the movies whether the review is positive or whether is negative and what are the negative points that people are mentioning about and so on so forth and. This again a classification task or you could use it for doing churn prediction where you are going to say whether a customer who is in the system is likely to leave your system is going to continue using your product or using your service for a longer period of time, so this is essentially churn so when a person leaves your services you call the person earner and you can label what the person is Channel or not and I have been giving you examples form medical diagnosis all through apart from actually diagnosing whether a person has the disease or not you could also use it for risk analysis in the slightly indirect way I talked about that when we when we do the algorithms for classification. So we talked about how we are interested in learning different lines or curves that can separate different classes in supervised learning and, so this curves can be represented using different structures and throughout the course we will be looking at different kinds of learning mechanisms like artificial neural networks support vector machines decision trees nearest neighbors and Bayesian networks and these are some of the popular ones and we look at these in more detail as the course progresses so another supervised learning problem is the one of prediction. (Refer Slide Time: 16:45) Or regression where the output that you are going to predict is no longer a discrete value it is not like we will buy a computer whereas not buy a computer it is more of a continuous value so here is an example, where at different times of day you have recorded the temperature so the input to the system is going to be the time of day and the output from the system is going to be the temperature that was measured at a particular point at the time right so you are going to get your experience or your training data is going to take this form so the blue points would be your input and the red points would be the outputs that you are expected to predict. So note here that the outputs are continuous or real value right and so you could think of this in this toy example as points to the left being day and the points to the right being night right and just as in the previous case of classification, so we could try to do these simple as possible fit in this case which would be to draw a straight line that is as close as possible to these points now you do see that like in the classification case when it choose a simple solution there are certain points at which we are making large errors right so we could try to fix that.



And try to do something more fancy but you can see that while the daytime temperatures are more or less fine with the night times we seem to be doing something really off right because we are going off too much to thee the right-hand side all right how are you could do something more complex just like in the classification case where we wanted to get that one point right so we could try and fit all these temperatures that were given to us by looking at a sufficiently complex curve. And again this as we discussed earlier is probably not the right answer and you are probably in this case surprisingly or better off fitting the straight line right and so these kinds of solutions where we trying to fit the noise in the data we are trying to make the solution predict the noise in the training data correctly are known as over fitting over fit solutions and one of the things that we look to avoid in, in machine learning is to over fit to the training data. (Refer Slide Time: 19:21) So we will talk about this again and then new course right and so what we do is typically we would like to do what is called linear regression some of you might have come across this and of different circumstances and the typical aim in linear regression is to say take the error that your line is making so if you take an example point let us say I take any let us say I take an example point somewhere here right. So this is the actual training data that is given to you and this is the prediction that your line is making at this point so this quantity is essentially the, the prediction error that this line is making and so what you do is you try to find that line that has the least prediction error right so you take the square of the errors that your prediction is making and then you try to minimize the, the sum of the squares of the errors why do we take the squares. (Refer Slide Time: 20:31) Because errors could be both positive or negative and we want to make sure that you are minimizing that regardless of the sign of the error okay and so with sufficient data right so a linear regression is simple enough you could just already using matrix inversions as we will see later but with many dimensions like the challenge is to avoid over fitting like we talked about earlier and then there are many ways of avoiding this. And so I will again talk about this in detail when we look at linear regression right so one point that I want to make is that linear regression is not as simple as it sounds right so here is an example so I have two input variables x1 and x2 right and if I try to fit a straight line with x1 and x2 I will probably end up with something like a1 x1 plus a2 x2 right and that looks like, like a plane in two dimensions right. But then if I just take these two dimensions and then transform them transform the input so instead of saying just the x1 and x2 if I say my input is going to look like x1 square x2 squared x1 x2 and then the x1 and x2 s it was in the beginning so instead of looking at a two-dimensional input if I am going to look at a 5 dimensional input right. So that will and out now I am going to fit a line or a linear plane in this 5 dimensional input so that will be like a1 x1 squared plus a2 x2 square plus a3 x1 x2 plus a4 x1 plus a5 x2 now that is no longer the equation of a line in two dimensions right so that is the equation of a second-order polynomial in two dimensions but I can still think of this as doing linear regression because I am only fitting a function that is going to be linear in the input variables right so by choosing an appropriate transformation of the inputs. (Refer Slide Time: 22:38) I can fit any higher-order function so I could solve very complex problems using linear regression and so it is not really a weak method as you would think at first, first glance again we will look at this in slightly more detail in the later lectures right and regression our prediction can be applied in a variety of places one popular places in time series prediction you could think about predicting rainfall in a certain region or how much you are going to spend on your telephone calls you could think of doing even classification using this.



If you think of you remember our encoding of plus 1 and minus 1 for the class labels so you could think of plus 1 and minus 1 as the outputs right and then you can fit a regression line regression curve to that and if the output is greater than 0 you would say this classis plus 1 its output is less than 0 you see the class is minus 1 so it could use the regression ideas to fitness will solve the classification problem and you could also do data addiction. So I really do not want to you know give you all the millions of data points that I have in my data set but what I would do is essentially fit the curve to that and then give you just the coefficients of the curve right. And more often than not that is sufficient for us to get a sense of the data and that brings us to the next application I have listed their which is trend analysis so I am not really interested in quite many times. I am not interested in the actual values of the data but more in the, the trends so for example I have a solution that I am trying to measure the running times off and I am not really interested in the actual running time because with 37seconds to 38 seconds is not going to tell me much. But I would really like to know if the running time scales linearly or exponentially with the size of the important all right so those kinds of analysis again can be done using regression and in the last one here is again risk factor analysis like we had in classification and you can look at which are the factors that contribute most to the output so that brings us to the end of this module on supervised learning,,

Hello and welcome to this module on introduction to unsupervised learning, right. So in supervised learning we looked at how you will handle training data that had labels on it. (Refer Slide Time: 00:26) So this is this particular place this is a classification data set where red denotes one class and blue denotes the other class right. (Refer Slide Time: 00:35) And in unsupervised learning right so you basically have a lot of data that is given to you but they do not have any labels attached to them right so we look at first at the problem of clustering where your goal is to find groups of coherent or cohesive data points in this input space right so here is an example of possible clusters. (Refer Slide Time: 00:57) So those set of data points could form a cluster right and again now those set of data points could form a cluster and again those and those so there are like four clusters that we have identified in this in this setup so one thing to note here is that even in something like clustering so I need to have some form of a bias right so in this case the bias that I am having is in the shape of the cluster so I am assuming that the clusters are all ellipsoids right and therefore you know I have been drawing a specific shape curves for representing the clusters.



And also note that not all data points need to fall into clusters and there are a couple of points there that do not fall into any of the clusters this is primarily a artifact of me assuming that they are ellipsoids but still there are other points in the center is actually faraway from all the other points in the in the data set to be considered as what are known as outliers so when you do clustering so there are two things so one is you are interested in finding cohesive groups of points and the second is you are also interested in finding data points that do not conform to the patterns in the input and these are known as outliers all right. (Refer Slide Time: 02:23) And that is as many mean different ways of an which you can accomplish clustering and we will look at a few in the course and the applications are numerous right so here are a few representative ones so one thing is to look at customer data right and try to discover the classes of customers you know there are so earlier we looked at in the supervised learning case we looked at is that a customer will buy a computer or will not buy a computer as opposed to that we could just take all the customer data that you have and try to just group them into different kinds of customers who come to your shop and then you could do some kind of targeted promotions and different classes of customers right. And this need not necessarily come with labels you know I am not going to tell you that okay this customer is class 1 that customer is class 2 you are just going to find out which of the customers are more similar with each other all right. And as the second application which you have illustrated here is that I could do clustering on image pixels so that you could discover different regions in the image and then you could do some segmentation based on that different region so for example here it have a picture of a picture of a beach scene and then you are able to figure out the clouds and the sand and the sea and the tree from the image so that allows you to make more sense out of the image right.



Or you could do clustering on world usages right and you could discover synonyms and you could also do clustering on documents right and depending on which kind of documents are similar to each other and if I give you a collection of say 100,000 documents I might be able to figure out what are the different topics that are discussed in this collection of documents and many ways in which you can use clustering rule mining. (Refer Slide Time: 04:17)


And as I should give you a site about the usage of the word mining here so many of you might have heard of the term data mining and more often than not the purported data mining tasks are essentially machine learning problems right so it could be classification regression and so on so forth and the first problem that was essentially introduced as a mining problem and not as a learning problem was the one of mining frequent patterns and associations and that is one of the reasons


I call this Association rule mining as opposed to Association rule learning just to keep the historic connection intact right, so in Association rule mining we are interested in finding frequent patterns that occur in the input data and then we are looking at conditional dependencies among these patterns right.


And so for example if A and B occur together often right then I could say something like if A happens then B will happen let us suppose that so you have customers that are coming to your shop and whenever customer A visits your shop custom B also tags along with him right, so the next time you find customary


A somewhere in the shop so you can know that customer B is already there in the shop along with A. Or with very high confidence you could say that B is also in the shop at some somewhere else maybe not with A but somewhere else in the shop all right, so these are the kinds of rules that we are looking at Association rules which are conditional dependencies if A has come then B is also there right and so the Association rule mining process usually goes in two stages so the first thing is we find all frequent patterns. So A happens often so A is a customer that comes to measure the store often right and then I find that A and B are paths of customers that come to my store often so if I once I have that right A comes to my store often an A and B comes to my store often then I can derive associations from this kind this frequent patterns right and also you could do this in the variety of different settings you could find sequences in time series data right and where you could look at triggers for certain events. Or you could look at fault analysis right by looking at a sequence of events that happened and you can figure out which event occurs more often with the fault right or you could look at transactions data which is the most popular example given here is what is called Market Basket data so you go to a shop and you buy a bunch of things together and you put them in your basket so what is there in your basket right so this forms the transaction so you buy say eggs, milk and bread and so all of this go together in your basket. And then you can find out what are the frequently occurring patterns in this purchase data and then you can make rules out of those or you could look at finding patterns and graphs that is typically used in social network analysis so which kind of interactions among entities happen often right so that is a that is another question that is what we looking at right. (Refer Slide Time: 07:31) So the most popular thing here is mining transactions so the most popular application here is mining transactions and as I mentioned earlier transaction is a collection of items that are bought together right and so here is a little bit of terminology and it is a set or a subset of items is often called an item set in the Association rule mining community and so the first step that you have to do is find frequent item sets right. And you can conclude that item set A if it is frequent implies item set B if both A and AUB or frequent item sets right so A and B are subset so AUB is another subset so if both A and AUB or frequent item sets then you can say that item set A implies item set B right and like I mentioned earlier so there are many applications here so you could think of predicting co-occurrence of events. (Refer Slide Time: 08:31) And Market Basket analysis and type series analysis like I mentioned earlier you could think of trigger events or false causes of False and so on so forth right so this brings us to the end of this module introducing unsupervised learning.

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