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Data Science And Machine Learning With Python And Tensorflow
Last updated 8/2019
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 64.64 GB | Duration: 114h 33m
Create Apps using Machine learning and Data Science to Create Visual Diagrams and graphic bars with Python!
What you'll learn
Create apps with Python
Learn Java language fundamentals
Read finance data directly from Yahoo
Train and test a model and use it for future predictions
Customise our graphs with visuals, a title, labels, text and a legend
Understand basic 3D plotting
Build apps, learn PyCharm, Android Studio, Machine Learning, TensorFlow models, TensorBoard, and so much more in this epic artificial intelligence course
Requirements
Download Anaconda 4.2.0, the free data science platform by Continuum, which contains Python 3.5.2 and Matplotlib.
Otherwise, you can download and install Python 3.5.2 and Matplotlib for free online.
Topics involve intermediate math, so familiarity with university-level math is helpful.
Description
We at Mammoth Interactive value input from students like you. Feel free to leave us your feedback. Machine learning is a way for a program to analyze previous data (or past experiences) to make decisions or predict the future.This course was funded through a massively successful Kickstarter campaign.We use frameworks like TensorFlow that make it easy to build, train, test, and use machine learning models. TensorFlow makes machine learning so much more accessible to programmers everywhere.You can expect a complete and comprehensive course that guides you through the basics, then through some simple models. You will end up with a portfolio of apps driven by machine learning, as well as the know-how to create more and expand upon what we build together. Tools, tips, and tricks (with Android support, Python & Java)I start by teaching you the basics of the languages, programs, and underlying concepts of machine learning. You will become an expert ready to build your own machine learning-driven mobile apps, which are the future in mobile app development.Do you also want to learn how to visualize data? Enroll in this course to learn how to do so directly in code. In Part 1, you learn how to use Python, a popular coding language used for websites like YouTube and Instagram. You learn the basics of programming, including topics like variables, functions, and if statements. You learn about data structures such as lists, dictionaries, and sets. We cover how to use for and while loops, how to handle user input and output, file input and output. We apply our knowledge to build a fully functional tic-tac-toe game. You learn classes, methods, attributes, instancing, and class inheritance. We make an additional Blackjack game! You learn how to solve errors that can occur when you work as a programmer.In Part 2, you take your Python knowledge and apply it to Matplotlib. We go over many cool features of Matplotlib that we can use for data visualization. We show you how to make line plots, scatter plots, candlestick plots. You learn how to customize the visuals of your graph and to add text and annotate graphs. And much more!Why choose Mammoth Interactive?We prioritize learning by doing. We blend theory with practical projects to ensure you get a hands-on experience by building projects alongside your instructor. Our experienced instructors know how to explain topics clearly at a logical pace. Check out our huge catalog of courses for more content.Also now included in these bundles are our extra courses. If you want to learn to use other programs such as Camtasia or Sketch, you get more content than what you paid for this way!We really hope you decide to purchase this course and take your knowledge to the next level. Let's get started.Enroll now to join the Mammoth community!
Overview
Section 1: Intro to Android Studio
Lecture 1 Intro and Topics List
Lecture 2 Downloading and Installing Android Studio
Lecture 3 Exploring Interface
Lecture 4 Setting up an Emulator and Running Project
Lecture 5 Code
Section 2: Intro to Java
Lecture 6 Intro to Language Basics
Lecture 7 Variable Types
Lecture 8 Operations on Variables
Lecture 9 Array and Lists
Lecture 10 Array and List Operations
Lecture 11 If and Switch Statements
Lecture 12 While Loops
Lecture 13 For Loops
Lecture 14 Functions Intro
Lecture 15 Parameters and Return Values
Lecture 16 Classes and Objects Intro
Lecture 17 Superclass and Subclasses
Lecture 18 Static Variables and Axis Modifiers
Section 3: Intro to App Development
Lecture 19 Intro To Android App Development
Lecture 20 Building Basic UI
Lecture 21 Connecting UI to Backend
Lecture 22 Implementing Backend and Tidying UI
Section 4: Intro to ML Concepts
Lecture 23 Intro to ML
Lecture 24 Pycharm Files
Section 5: Introduction to PyCharm for Python
Lecture 25 Intro and Topics List
Lecture 26 Downloading and Installing Pycharm and Python
Lecture 27 Exploring the Pycharm Interface
Lecture 28 Support for Python Problems or Questions
Lecture 29 Learning Python with Mammoth Interactive
Section 6: Python Language Basics
Lecture 30 Intro to Variables
Lecture 31 Variables Operations and Conversions
Lecture 32 Collection Types
Lecture 33 Collections Operations
Lecture 34 Control Flow If Statements
Lecture 35 While and For Loops
Lecture 36 Functions
Lecture 37 Classes and Objects
Section 7: Intro to Tensorflow
Lecture 38 Intro
Lecture 39 Topics List
Lecture 40 Installing TensorFlow
Lecture 41 Importing Tensorflow to Pycharm
Lecture 42 Constant Nodes and Sessions
Lecture 43 Variable Nodes
Lecture 44 Placeholder Nodes
Lecture 45 Operation nodes
Lecture 46 Loss, Optimizers, and Training
Lecture 47 Building a Linear Regression Model
Lecture 48 Source Files
Section 8: Machine Learning in Android Studio Projects
Lecture 49 Coming Up - Machine Learning in Android Studio Projects
Section 9: Tensorflow Estimator
Lecture 50 Introduction
Lecture 51 Topics List
Lecture 52 Setting up Prebuilt Estimator Model
Lecture 53 Evaluating and Predicting with Prebuilt Model
Lecture 54 Building Custom Estimator Function
Lecture 55 Testing the Custom Estimator Function
Lecture 56 Summary and Model Comparison
Lecture 57 Source Files
Section 10: Intro to Android Machine Learning Model Import
Lecture 58 Intro and Demo
Lecture 59 Topics List
Lecture 60 Formatting and Saving the Model
Lecture 61 Saving the Optimized Graph File
Lecture 62 Starting Android Project
Lecture 63 Building the UI
Lecture 64 Implementing Inference Functionality
Lecture 65 Testing and Error Fixing
Lecture 66 Source Files
Section 11: Simple MNIST
Lecture 67 Intro and Demo
Lecture 68 Topics List and Intro to MNIST Data
Lecture 69 Building Computational Graph
Lecture 70 Training and Testing the Model
Lecture 71 Saving and Freezing the Graph for Android Import
Lecture 72 Setting up Android Studio Project
Lecture 73 Building the UI
Lecture 74 Loading Digit Images
Lecture 75 Formatting Image Data
Lecture 76 Making Prediction Using Model
Lecture 77 Displaying Results and Summary
Lecture 78 Simple MNIST - Mammoth Interactive
Section 12: MNIST with Estimator
Lecture 79 Introduction
Lecture 80 Topics List
Lecture 81 Building Custom Estimator Function
Lecture 82 Building Input Functions, Training, and Testing
Lecture 83 Predicting Using Our Model and Model Comparisons
Lecture 84 MNIST With Estimator - Mammoth Interactive
Section 13: Advanced MNIST
Lecture 85 Intro and Demo
Lecture 86 Topics List
Lecture 87 Building Neuron Functions
Lecture 88 Building the Convolutional Layers
Lecture 89 Building Dense, Dropout, and Readout Layers
Lecture 90 Writing Loss and Optimizer Functions and Training and Testing
Lecture 91 Optimizing Saved Graph
Lecture 92 Setting up Android Project
Lecture 93 Setting Up UI
Lecture 94 Load and Display Digit Images
Lecture 95 Formatting Model Input
Lecture 96 Displaying Results and Summary
Lecture 97 Source Files
Section 14: Intro to Tensorboard
Lecture 98 Introduction
Lecture 99 Examining Computational Graph In Tensorboard
Lecture 100 Analyzing Scalars and Histograms
Lecture 101 Modifying Model Parameters Across Multiple Runs
Lecture 102 Source Code
Section 15: Increase Efficiency of Machine Learning Models
Lecture 103 Coming Up - Building Efficient Models
Lecture 104 Intro to Tensorflow Lite
Lecture 105 Source Code
Section 16: Text Summarizer
Lecture 106 Introduction
Lecture 107 Exploring How Model Is Built
Lecture 108 Exploring Training and Summarizing Mechanisms
Lecture 109 Exploring Training and Summarizing Code
Lecture 110 Testing the Model
Lecture 111 Text Summarizer Pycharm Source Files
Section 17: Object Localization
Lecture 112 Introductions
Lecture 113 Examining Project Code
Lecture 114 Testing with a Mobile Device
Section 18: Object Recognition
Lecture 115 Introduction
Lecture 116 Examining Code
Lecture 117 Testing on Mobile Device
Section 19: Introduction to Python Programming
Lecture 118 Introduction to Python
Lecture 119 Variables
Lecture 120 Functions
Lecture 121 if Statements
Section 20: Lists
Lecture 122 Introduction to Lists
Section 21: Loops
Lecture 123 Introduction to and Examples of using Loops
Lecture 124 Getting familiar with while Loops
Lecture 125 Breaking and Continuing in Loops
Lecture 126 Making Shapes with Loops
Lecture 127 Nested Loops and Printing a Tic-Tac-Toe field
Section 22: Sets and Dictionaries
Lecture 128 Understanding Sets and Dictionaries
Lecture 129 An Example for an Invetory List
Section 23: Input and Output
Lecture 130 Introduction and Implementation of Input and Output
Lecture 131 Introduction to and Integrating File Input and Output
Lecture 132 An example for a Tic-Tac-Toe Game
Lecture 133 An example of a Tic-Tac-Toe Game (Cont'd)
Lecture 134 An Example writing Participant data to File
Lecture 135 An Example Reading Participant Data from File
Lecture 136 Doing some simple statistics with Participant data from File
Section 24: Classes
Lecture 137 A First Look at Classes
Lecture 138 Inheritance and Classes
Lecture 139 An Example of Classes using Pets
Lecture 140 An Example of Classes using Pets - Dogs
Lecture 141 An examples of Classes using Pets - Cats
Lecture 142 Taking The Pets Example further and adding humans
Section 25: Importing
Lecture 143 Introduction to Importing and the Random Library
Lecture 144 Another way of importing and using lists with random
Lecture 145 Using the Time Library
Lecture 146 Introduction to The Math Library
Lecture 147 Creating a User guessing Game with Random
Lecture 148 Making the Computer guess a random number
Section 26: Project Blackjack Game
Lecture 149 BlackJack Game Part 1 - Creating and Shuffling a Deck
Lecture 150 Blackjack Game Part 2 - Creating the player class
Lecture 151 Blackjack Game Part 3 - Expanding the Player Class
Lecture 152 Blackjack Game Part 4 - Implementing a bet and win
Lecture 153 Blackjack Game Part 5 - Implementing the player moves
Lecture 154 Blackjack Game Part 6 - Running the Game (Final)
Section 27: Error Handling
Lecture 155 Getting started with error handling
Section 28: Matplotlib Fundamentals
Lecture 156 Introduction to Matplotlib
Lecture 157 Setup and Installation
Lecture 158 Creating Our First Scatter Plot
Lecture 159 Line Plots
Section 29: Graph Customization
Lecture 160 Labels, Title, and a Legend
Lecture 161 Changing The Axis Ticks
Lecture 162 Adding text into our graphs
Lecture 163 Annotating our graph
Lecture 164 Changing Figure Size and Saving the Figure
Lecture 165 Changing the axis scales
Section 30: Advanced Plots
Lecture 166 Creating Histograms
Lecture 167 Building More Histograms
Lecture 168 Changing Histogram Types
Lecture 169 Bar Plots
Lecture 170 Stack Plots
Lecture 171 Pie Charts
Lecture 172 Box And Whisker Plots
Section 31: Finance Graphs
Lecture 173 Creating Figures and Subplots
Lecture 174 Getting and Parsing csv data for plotting
Lecture 175 Creating a Candlestick plot
Lecture 176 Setting Dates for our Candlestick Plot
Lecture 177 Reading data directly from Yahoo
Lecture 178 Customizing our OHLC graph
Section 32: Advanced Graph Customization
Lecture 179 Adding Grids
Lecture 180 Taking a Closer Look at Tick Labels
Lecture 181 Customising Grid Lines
Lecture 182 Live Graphs
Lecture 183 Styles and rcParameters
Lecture 184 Sharing an X axis between two plots
Lecture 185 Setting Axis Spines
Lecture 186 Creating Multiple Axes in Our Figure
Lecture 187 Creating Multiple Axes in Our Figure (cont'd)
Lecture 188 Plotting into the Multiple Axes
Lecture 189 Plotting into the Multiple Axes (cont'd)
Section 33: 3D Plotting
Lecture 190 Getting started with 3D plotting
Lecture 191 Surface Plots and Colormaps
Lecture 192 Wireframes and Contour Plots
Lecture 193 Stacks of Histograms and Text for 3D Plotting
Section 34: Sketch
Lecture 194 Course Intro and Sketch Tools
Lecture 195 Sketch Files - Sketch Tools
Lecture 196 Sketch Basics and Online Resources
Lecture 197 Plug-ins and Designing your First Mobile app
Lecture 198 Your First Mobile App Continued
Lecture 199 Sketch Files - Your First Mobile App
Lecture 200 Shortcuts and Extra tips
Lecture 201 Sketch Files - Shortcuts by Mammoth Interactive
Section 35: Learn to Code in HTML
Lecture 202 Intro to HTML
Lecture 203 Writing our first HTML
Lecture 204 Intro to Lists and Comments
Lecture 205 Nested Lists
Lecture 206 Loading Images
Lecture 207 Loading Images in Lists
Lecture 208 Links
Lecture 209 Images as Link
Lecture 210 Mailto Link
Lecture 211 Div Element
Section 36: Learn to Code in CSS
Lecture 212 Introduction
Lecture 213 Introducing the Box Model
Lecture 214 Writing our First CSS
Lecture 215 More CSS Examples
Lecture 216 Inheritance
Lecture 217 More on Type Selectors
Lecture 218 Getting Direct Descendents
Lecture 219 Class Intro
Lecture 220 Multiple Classes
Lecture 221 id Intro
Lecture 222 CSS Specificity
Lecture 223 Selecting Multiple Pseudo Classes and Sibling Matching
Lecture 224 Styling Recipe Page
Lecture 225 Loading External Stylesheet
Section 37: D3.js
Lecture 226 Introduction to Course and D3
Lecture 227 Source Code
Lecture 228 Handling Data and Your First Project
Lecture 229 Source code
Lecture 230 Continuing your First Project
Lecture 231 Understanding Scale
Lecture 232 Source Code
Lecture 233 Complex charts, Animations and Interactivity
Lecture 234 Source Code
Section 38: Flask
Lecture 235 Setting Up and Basic Flask
Lecture 236 Setting up Terminals on Windows 7 and Mac
Lecture 237 Terminal basic commands and symbols
Lecture 238 Source Code - Setting up Flask
Lecture 239 Source Code - Basic Flask HTML & CSS
Lecture 240 Basic Flask Database
Lecture 241 Source Code - Basic Flask Database
Lecture 242 Flask Session and Resources
Lecture 243 Source Code - Flask Session
Lecture 244 Flask Digital Ocean
Lecture 245 Flask Digital Ocean Continued
Section 39: Xcode Fundamentals
Lecture 246 Intro and Demo
Lecture 247 General Interface
Lecture 248 Files System
Lecture 249 ViewController
Lecture 250 Storyboard File
Lecture 251 Connecting Outlets and Actions
Lecture 252 Running an Application
Lecture 253 Debugging an Application
Lecture 254 Source Code and Art Assets
Section 40: Swift 4 Language Basics
Lecture 255 Language Basics Topics List
Section 41: Variable and Constants
Lecture 256 Learning Goals
Lecture 257 Intro to Variables and Constants
Lecture 258 Primitive types
Lecture 259 Strings
Lecture 260 Nil Values
Lecture 261 Tuples
Lecture 262 Type Conversions
Lecture 263 Assignment Operators
Lecture 264 Conditional Operators
Lecture 265 Variables and Constants Text.playground
Section 42: Collection Types
Lecture 266 Topics List and Learning Objectives
Lecture 267 Intro to Collection Types
Lecture 268 Creating Arrays
Lecture 269 Common Array Operations
Lecture 270 Multidimensional Arrays
Lecture 271 Ranges
Lecture 272 Collection Types Text.playground
Section 43: Control flow
Lecture 273 Topics List and Learning Objectives
Lecture 274 Intro to If and Else Statements
Lecture 275 Else If Statements
Lecture 276 Multiple Simultaneous Tests
Lecture 277 Intro To Switch Statements
Lecture 278 Advanced Switch Statement Techniques
Lecture 279 Testing for Nil Values
Lecture 280 Intro to While Loops
Lecture 281 Intro to for...in Loops
Lecture 282 Intro to For...In Loops (Cont'd)
Lecture 283 Complex Loops and Loop Control statements
Lecture 284 Control Flow Text.playground
Section 44: Functions
Lecture 285 Intro to Functions
Lecture 286 Function Parameters
Lecture 287 Return Statements
Lecture 288 Parameter Variations - Argument Labels
Lecture 289 Parameter Variations - Default Values
Lecture 290 Parameters Variations - InOut Parameters
Lecture 291 Parameter Variations - Variadic Parameters
Lecture 292 Returning Multiple Values Simultaneously
Lecture 293 Functions Text.playground
Section 45: Classes, Struct and Enums
Lecture 294 Topics List and Learning Objectives
Lecture 295 Intro to Classes
Lecture 296 Properties as fields - Add to Class Implementation
Lecture 297 Custom Getters and Setters
Lecture 298 Calculated Properties
Lecture 299 Variable Scope and Self
Lecture 300 Lazy and Static Variables
Lecture 301 Behaviour as Instance Methods and Class type Methods
Lecture 302 Behaviour and Instance Methods
Lecture 303 Class Type Methods
Lecture 304 Class Instances as Field Variables
Lecture 305 Inheritance, Subclassing and SuperClassing
Lecture 306 Overriding Initializers
Lecture 307 Overriding Properties
Lecture 308 Overriding Methods
Lecture 309 Structs Overview
Lecture 310 Enumerations
Lecture 311 Comparisons between Classes, Structs and Enums
Lecture 312 Classes, Structs, Enums Text.playground
Section 46: Practical MacOS BootCamps
Lecture 313 Introduction and UI Elements
Lecture 314 Calculator Setup and Tax Calculator
Lecture 315 Calculate Tax And Tip - Mammoth Interactive Source Code
Lecture 316 Tip Calculator and View Controller
Lecture 317 View Controller - Mammoth Interactive Source Code
Lecture 318 Constraints
Lecture 319 Constraints - Mammoth Interactive Source Code
Lecture 320 Constraints Code
Lecture 321 Refactor
Lecture 322 Refactor - Mammoth Interactive Source Code
Lecture 323 MacOsElements - Mammoth Interactive Source Code
Section 47: Data Mining With Python
Lecture 324 Data Wrangling and Section 1
Lecture 325 Project Files - Data Mining with Mammoth Interactive
Lecture 326 Project Files - Data Wrangling with Mammoth Interactive
Lecture 327 Data Mining Fundamentals
Lecture 328 Project Files - Data Mining fundamentals with Mammoth Interactive
Lecture 329 Framework Explained, Taming Big Bank with Data
Lecture 330 Project Files - Frameworks with Mammoth Interactive
Lecture 331 Mining and Storing Data
Lecture 332 Project Files - Mining and Storing with Mammoth Interactive
Lecture 333 NLP (Natural Language Processing)
Lecture 334 Project Files - NLP with Mammoth Interactive
Lecture 335 Summary Challenge
Lecture 336 Conclusion Files - Mammoth Interactive
Section 48: Introduction to Video Editing
Lecture 337 Introduction to the Course
Lecture 338 Installing Camtasia
Lecture 339 Exploring the Interface
Lecture 340 Camtasia Project Files
Section 49: Setting Up a Screen Recording
Lecture 341 Introduction and Tips for Recording
Lecture 342 Creating a Recording Account
Lecture 343 Full Screen vs Window Mode
Lecture 344 Setting the Recording Resolution
Lecture 345 Different Resolutions and their Uses
Lecture 346 Tips to Improve Recording Quality and Summary
Section 50: Camtasia Recording
Lecture 347 Introduction and Workflow
Lecture 348 Tools Options Menu
Lecture 349 Your First Recording
Lecture 350 Viewing your Test
Lecture 351 Challenge - VIDEO GAME NARRATION
Lecture 352 Mic Etiqutte
Lecture 353 Project - Recording Exercise
Lecture 354 Webcam, Telprompter, and Summary
Section 51: Camtasia Screen Layout
Lecture 355 Introduction and Tools Panel
Lecture 356 Canvas
Lecture 357 Zoom N Pan
Lecture 358 Annotations
Lecture 359 Yellow Snap Lines
Lecture 360 TimeLine Basics, Summary and Challenge
Section 52: Camtasia Editing
Lecture 361 Introduction and Importing Media
Lecture 362 Markers
Lecture 363 Split
Lecture 364 Working with Audio
Lecture 365 Clip Speed
Lecture 366 Locking and Disabling tracks
Lecture 367 Transitions
Lecture 368 Working with Images
Lecture 369 Voice Narration
Lecture 370 Noise Removal
Lecture 371 Smart Focus
Lecture 372 Summary and Challenge
Section 53: Advance Editing Introduction
Lecture 373 Advance Editing Introduction
Lecture 374 Zooming Multiple Tracks
Lecture 375 Easing
Lecture 376 Animations
Lecture 377 Behaviors
Lecture 378 Color Adjustment
Lecture 379 Clip Speed
Lecture 380 Remove a Color
Lecture 381 Device Frame
Lecture 382 Theme Manager
Lecture 383 Libraries
Lecture 384 Media and Summary
Section 54: Camtasia Resources and Tips
Lecture 385 Resources and Tips Introduction
Lecture 386 Masking
Lecture 387 Extending Frames
Lecture 388 Working with Video
Section 55: Exporting a Project for Youtube
Lecture 389 Exporting a Project for Youtube
Section 56: Code with C#
Lecture 390 Introduction to Course and Types
Lecture 391 Operator, Classes , and Additional Types
Lecture 392 Statements & Loops
Lecture 393 Arrays, Lists, and Strings
Lecture 394 Files, Directories, and Debugs
Lecture 395 Source code
Section 57: Learn to Code with R
Lecture 396 Intro to R
Lecture 397 Control Flow and Core Concepts
Lecture 398 Matrices, Dataframes, Lists and Data Manipulation
Lecture 399 GGplot and Intro to Machine learning
Lecture 400 Conclusion
Lecture 401 Source Code
Section 58: Advanced R
Lecture 402 Course Overview and Data Setup
Lecture 403 Source Code - Setting Up Data - Mammoth Interactive
Lecture 404 Functions in R
Lecture 405 Source Code - Functions - Mammoth Interactive
Lecture 406 Regression Model
Lecture 407 Source Code - Regression Models - Mammoth Interactive
Lecture 408 Regression Models Continued and Classification Models
Lecture 409 Source Code - Classification Models - Mammoth Interactive
Lecture 410 Classification Models Continued, RMark Down and Excel
Lecture 411 Source Code - RMarkDown And Excel - Mammoth Interactive
Lecture 412 Datasets - Mammoth Interactive
Section 59: Learn to Code with Java
Lecture 413 Introduction and setting up Android Studio
Lecture 414 Introduction - Encryption Source Code
Lecture 415 Setting up Continued
Lecture 416 Java Programming Fundamentals
Lecture 417 Source Code - Java Programming Fundamentals
Lecture 418 Additional Java fundamentals
Lecture 419 Source Code - Additional fundamentals
Lecture 420 Classes
Lecture 421 Source Code - Classes
Lecture 422 Please rate this course
Lecture 423 Bonus Course
People who want to learn machine learning concepts through practical projects with PyCharm, Python, Android Studio, Java, and TensorFlow,Absolute beginners who want to learn to code for the web in the popular Python programming language and use data science to make graphs.,Anyone who wants to learn the technology that is shaping how we interact with the world around us,Anyone who wants to use data for prediction, recognition, and classification,Experienced programmers who want to learn a 2D plotting library for Python.
Code:
https://anonymz.com/?https://www.udemy.com/course/data-science-and-machine-learning-with-python-and-tensorflow/
![[Image: datascienceandmachinepji6w.jpg]](https://abload.de/img/datascienceandmachinepji6w.jpg)
Code:
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Code:
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