04.12.2022, 22:06
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Learn To Master Python: From Beginner To Expert
Last updated 4/2019
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 65.56 GB | Duration: 115h 31m
Use Google's deep learning framework TensorFlow with Python. Leverage machine learning to make game changing apps.
What you'll learn
Code in the Python programming language.
Create basic line and scatter plots with Matplotlib
Customize our graphs with visuals, a title, labels, text and a legend.
Optimize for intelligent apps: discover the TensorFlow mobile framework and build scientific analysis apps
Machine learning goes mobile: learn how to incorporate machine learning models into Android apps
And More!
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.
This course was recorded on a Mac, but you can use a PC
Description
We at Mammoth Interactive value input from students like you. Feel free to leave us your feedback.This course was funded through a massively successful Kickstarter campaign.Learn artificial intelligence, machine learning, and mobile dev with Java, Android, TensorFlow Estimator, PyCharm, and MNIST. Woah! That's a lot of content for one course.Use Google's deep learning framework TensorFlow with Python. Leverage machine learning to improve your appsPrediction Models MasterclassBy the end of this course you will have 3 complete mobile machine learning models and apps. We will build a simple weather prediction project, stock market prediction project, and text-response project. For each we will build a basic version in PyCharm, save the trained model, export the trained model to Android Studio, and build an app around model.No experience? No problemDo you want to learn how to visualize data? Enroll in this course to learn how to do so directly in code. Make 2D & 3D Graphs in Python with Matplotlib for Beginners! is suitable for coding beginners because we begin with a complete introduction to coding. Then we delve deep into using Matplotlib, a Python 2D plotting library.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: Make Predictions with Python Machine Learning for Apps - Resources
Lecture 1 Make Predictions with Python Machine Learning for Apps - Resources
Section 2: Introduction to Machine Learning and Software
Lecture 2 Source Code
Section 3: Intro to Android
Lecture 3 Intro and Topics List
Section 4: Intro to Android Studio
Lecture 4 Downloading and Installing Android Studio
Lecture 5 Exploring Interface
Lecture 6 Setting up an Emulator and Running Project
Section 5: Intro to Java
Lecture 7 Intro to Language Basics
Lecture 8 Variable Types
Lecture 9 Operations on Variables
Lecture 10 Array and Lists
Lecture 11 Array and List Operations
Lecture 12 If and Switch Statements
Lecture 13 While Loops
Lecture 14 For Loops
Lecture 15 Functions Intro
Lecture 16 Parameters and Return Values
Lecture 17 Classes and Objects Intro
Lecture 18 Superclass and Subclasses
Lecture 19 Static Variables and Axis Modifiers
Section 6: Intro to App Development
Lecture 20 Intro To Android App Development
Lecture 21 Building Basic UI
Lecture 22 Connecting UI to Backend
Lecture 23 Implementing Backend and Tidying UI
Section 7: Intro to ML Concepts
Lecture 24 Intro to ML
Lecture 25 Pycharm Files
Section 8: Intro to Pycharm
Lecture 26 Intro and Topics List
Lecture 27 Learning Python with Mammoth Interactive
Section 9: Introduction
Lecture 28 Downloading and Installing Pycharm and Python
Lecture 29 Support for Python Problems or Questions
Lecture 30 Exploring Pycharm
Section 10: Python Language Basics
Lecture 31 Intro to Variables
Lecture 32 Variables Operations and Conversions
Lecture 33 Collection Types
Lecture 34 Collections Operations
Lecture 35 Control Flow If Statements
Lecture 36 While and For Loops
Lecture 37 Functions
Lecture 38 Classes and Objects
Section 11: Intro to Tensorflow
Lecture 39 Intro
Lecture 40 Installing TensorFlow
Lecture 41 Topics List
Lecture 42 Importing Tensorflow to Pycharm
Lecture 43 Constant Nodes and Sessions
Lecture 44 Variable Nodes
Lecture 45 Placeholder Nodes
Lecture 46 Operation nodes
Lecture 47 Loss, Optimizers, and Training
Lecture 48 Building a Linear Regression Model
Lecture 49 Source Files
Section 12: Machine Learning in Android Studio Projects
Lecture 50 Introduction to Level 2
Section 13: Tensorflow Estimator
Lecture 51 Introduction
Lecture 52 Topics List
Lecture 53 Setting up Prebuilt Estimator Model
Lecture 54 Evaluating and Predicting with Prebuilt Model
Lecture 55 Building Custom Estimator Function
Lecture 56 Testing the Custom Estimator Function
Lecture 57 Summary and Model Comparison
Lecture 58 Source Files
Section 14: Intro to Android Machine Learning Model Import
Lecture 59 Intro and Demo
Lecture 60 Topics List
Lecture 61 Formatting and Saving the Model
Lecture 62 Saving the Optimized Graph File
Lecture 63 Starting Android Project
Lecture 64 Building the UI
Lecture 65 Implementing Inference Functionality
Lecture 66 Testing and Error Fixing
Lecture 67 Source Files
Section 15: Simple MNIST
Lecture 68 Intro and Demo
Lecture 69 Topics List and Intro to MNIST Data
Lecture 70 Building Computational Graph
Lecture 71 Training and Testing the Model
Lecture 72 Saving and Freezing the Graph for Android Import
Lecture 73 Setting up Android Studio Project
Lecture 74 Building the UI
Lecture 75 Loading Digit Images
Lecture 76 Formatting Image Data
Lecture 77 Making Prediction Using Model
Lecture 78 Displaying Results and Summary
Lecture 79 Simple MNIST - Mammoth Interactive
Section 16: MNIST with Estimator
Lecture 80 Introduction
Lecture 81 Topics List
Lecture 82 Building Custom Estimator Function
Lecture 83 Building Input Functions, Training, and Testing
Lecture 84 Predicting Using Our Model and Model Comparisons
Lecture 85 MNIST With Estimator - Mammoth Interactive
Section 17: Build Image Recognition Apps
Lecture 86 Introduction to Level 3
Lecture 87 Source Code
Section 18: Stock Market Prediction
Lecture 88 Project Demo
Lecture 89 Project Overview
Lecture 90 Retrieving Data via RESTful API Call
Lecture 91 Parsing JSON Data Pycharm Style
Lecture 92 Formatting Data
Lecture 93 Building the Model
Lecture 94 Training and Testing The model
Lecture 95 Freezing Graph
Lecture 96 Setting up Android Project
Lecture 97 Building UI
Lecture 98 Requesting Data Via AsyncTask
Lecture 99 Parsing JSON Data Android Style
Lecture 100 Running Inference and Displaying Results
Lecture 101 Stock Market Prediction Project Files- Mammoth Interactive
Section 19: Text Prediction
Lecture 102 Intro and Demo
Lecture 103 Tasks List
Lecture 104 Processing Text Data
Lecture 105 Building Data Sets and Model Builder Function
Lecture 106 Building Computational Graph
Lecture 107 Writing Training and Testing Code
Lecture 108 Training, Testing, and Freezing Graph
Lecture 109 Setting up Android Project
Lecture 110 Setting up UI
Lecture 111 Setting up Vocab Dictionary
Lecture 112 Formatting Input and Running Through Model
Lecture 113 Text Prediction - Mammoth Interactive
Section 20: Weather Prediction
Lecture 114 Intro and Demo
Lecture 115 Tasks List
Lecture 116 Retrieving the Data
Lecture 117 Formatting Data Sets
Lecture 118 Building Computational Graph
Lecture 119 Writing Training, Testing, and Evaluating Functions
Lecture 120 Training, Testing, and Freezing the Model
Lecture 121 Setting up Android Project
Lecture 122 Building the UI
Lecture 123 Build App Backend and Project Summary
Lecture 124 Weather Prediction - Mammoth Interactive
Section 21: Introduction to Python Programming
Lecture 125 Introduction to Python
Lecture 126 Variables
Lecture 127 Functions
Lecture 128 if Statements
Section 22: Lists
Lecture 129 Introduction to Lists
Section 23: Loops
Lecture 130 Introduction to and Examples of using Loops
Lecture 131 Getting familiar with while Loops
Lecture 132 Breaking and Continuing in Loops
Lecture 133 Making Shapes with Loops
Lecture 134 Nested Loops and Printing a Tic-Tac-Toe field
Section 24: Sets and Dictionaries
Lecture 135 Understanding Sets and Dictionaries
Lecture 136 An Example for an Invetory List
Section 25: Input and Output
Lecture 137 Introduction and Implementation of Input and Output
Lecture 138 Introduction to and Integrating File Input and Output
Lecture 139 An example for a Tic-Tac-Toe Game
Lecture 140 An example of a Tic-Tac-Toe Game (Cont'd)
Lecture 141 An Example writing Participant data to File
Lecture 142 An Example Reading Participant Data from File
Lecture 143 Doing some simple statistics with Participant data from File
Section 26: Classes
Lecture 144 A First Look at Classes
Lecture 145 Inheritance and Classes
Lecture 146 An Example of Classes using Pets
Lecture 147 An Example of Classes using Pets - Dogs
Lecture 148 An examples of Classes using Pets - Cats
Lecture 149 Taking The Pets Example further and adding humans
Section 27: Importing
Lecture 150 Introduction to Importing and the Random Library
Lecture 151 Another way of importing and using lists with random
Lecture 152 Using the Time Library
Lecture 153 Introduction to The Math Library
Lecture 154 Creating a User guessing Game with Random
Lecture 155 Making the Computer guess a random number
Section 28: Project Blackjack Game
Lecture 156 BlackJack Game Part 1 - Creating and Shuffling a Deck
Lecture 157 Blackjack Game Part 2 - Creating the player class
Lecture 158 Blackjack Game Part 3 - Expanding the Player Class
Lecture 159 Blackjack Game Part 4 - Implementing a bet and win
Lecture 160 Blackjack Game part 5 - Implementing the player moves
Lecture 161 Blackjack Game Part 6 - Running the Game (Final)
Section 29: Error Handling
Lecture 162 Getting started with error handling
Section 30: Matplotlib Fundamentals
Lecture 163 Introduction to Matplotlib
Lecture 164 Setup and Installation
Lecture 165 Creating Our First Scatter Plot
Lecture 166 Line Plots
Section 31: Graph Customization
Lecture 167 Labels, Title, and a Legend
Lecture 168 Changing The Axis Ticks
Lecture 169 Adding text into our graphs
Lecture 170 Annotating our graph
Lecture 171 Changing Figure Size and Saving the Figure
Lecture 172 Changing the axis scales
Section 32: Advanced Plots
Lecture 173 Creating Histograms
Lecture 174 Building More Histograms
Lecture 175 Changing Histogram Types
Lecture 176 Bar Plots
Lecture 177 Stack Plots
Lecture 178 Pie Charts
Lecture 179 Box And Whisker Plots
Section 33: Finance Graphs
Lecture 180 Creating Figures and Subplots
Lecture 181 Getting and Parsing csv data for plotting
Lecture 182 Creating a Candlestick plot
Lecture 183 Setting Dates for our Candlestick Plot
Lecture 184 Reading data directly from Yahoo
Lecture 185 Customizing our OHLC graph
Section 34: Advanced Graph Customization
Lecture 186 Adding grids
Lecture 187 Taking a closer look at tick labels
Lecture 188 Customising grid lines
Lecture 189 Live Graphs
Lecture 190 Styles and rcParameters
Lecture 191 Sharing an X axis between two plots
Lecture 192 Setting Axis Spines
Lecture 193 Creating multiple axes in our figure
Lecture 194 Creating multiple axes in our figure (cont'd)
Lecture 195 Plotting into the multiple axes
Lecture 196 Plotting into the multiple axes (cont'd)
Section 35: 3D Plotting
Lecture 197 Getting started with 3D plotting
Lecture 198 Surface plots and colormaps
Lecture 199 Wireframes and contour plots
Lecture 200 Stacks of histograms and text for 3D plotting
Section 36: Sketch
Lecture 201 Course Intro and Sketch Tools
Lecture 202 Sketch Files - Sketch Tools
Lecture 203 Sketch Basics and Online Resources
Lecture 204 Plug-ins and Designing your First Mobile app
Lecture 205 Your First Mobile App Continued
Lecture 206 Sketch Files - Your First Mobile App
Lecture 207 Shortcuts and Extra tips
Lecture 208 Sketch Files - Shortcuts by Mammoth Interactive
Section 37: Learn to Code in HTML
Lecture 209 Intro to HTML
Lecture 210 Writing our first HTML
Lecture 211 Intro to Lists and Comments
Lecture 212 Nested Lists
Lecture 213 Loading Images
Lecture 214 Loading Images in Lists
Lecture 215 Links
Lecture 216 Images as Link
Lecture 217 Mailto Link
Lecture 218 Div Element
Section 38: Learn to Code in CSS
Lecture 219 Introduction
Lecture 220 Introducing the Box Model
Lecture 221 Writing our First CSS
Lecture 222 More CSS Examples
Lecture 223 Inheritance
Lecture 224 More on Type Selectors
Lecture 225 Getting Direct Descendents
Lecture 226 Class Intro
Lecture 227 Multiple Classes
Lecture 228 id Intro
Lecture 229 CSS Specificity
Lecture 230 Selecting Multiple Pseudo Classes and Sibling Matching
Lecture 231 Styling Recipe Page
Lecture 232 Loading External Stylesheet
Section 39: D3.js
Lecture 233 Introduction to Course and D3
Lecture 234 Source Code
Lecture 235 Handling Data and Your First Project
Lecture 236 Source code
Lecture 237 Continuing your First Project
Lecture 238 Understanding Scale
Lecture 239 Source Code
Lecture 240 Complex charts, Animations and Interactivity
Lecture 241 Source Code
Section 40: Flask
Lecture 242 Setting Up and Basic Flask
Lecture 243 Setting up Terminals on Windows 7 and Mac
Lecture 244 Terminal basic commands and symbols
Lecture 245 Source Code - Setting up Flask
Lecture 246 Source Code - Basic Flask HTML & CSS
Lecture 247 Basic Flask Database
Lecture 248 Source Code - Basic Flask Database
Lecture 249 Flask Session and Resources
Lecture 250 Source Code - Flask Session
Lecture 251 Flask Digital Ocean
Lecture 252 Flask Digital Ocean Continued
Section 41: Xcode Fundamentals
Lecture 253 Intro and Demo
Lecture 254 General Interface
Lecture 255 Files System
Lecture 256 ViewController
Lecture 257 Storyboard File
Lecture 258 Connecting Outlets and Actions
Lecture 259 Running an Application
Lecture 260 Debugging an Application
Lecture 261 Source Code and Art Assets
Section 42: Swift 4 Language Basics
Lecture 262 Language Basics Topics List
Section 43: Variable and Constants
Lecture 263 Learning Goals
Lecture 264 Intro to Variables and Constants
Lecture 265 Primitive types
Lecture 266 Strings
Lecture 267 Nil Values
Lecture 268 Tuples
Lecture 269 Type Conversions
Lecture 270 Assignment Operators
Lecture 271 Conditional Operators
Lecture 272 Variables and Constants Text.playground
Section 44: Collection Types
Lecture 273 Topics List and Learning Objectives
Lecture 274 Intro to Collection Types
Lecture 275 Creating Arrays
Lecture 276 Common Array Operations
Lecture 277 Multidimensional Arrays
Lecture 278 Ranges
Lecture 279 Collection Types Text.playground
Section 45: Control flow
Lecture 280 Topics List and Learning Objectives
Lecture 281 Intro to If and Else Statements
Lecture 282 Else If Statements
Lecture 283 Multiple Simultaneous Tests
Lecture 284 Intro To Switch Statements
Lecture 285 Advanced Switch Statement Techniques
Lecture 286 Testing for Nil Values
Lecture 287 Intro to While Loops
Lecture 288 Intro to for...in Loops
Lecture 289 Intro to For...In Loops (Cont'd)
Lecture 290 Complex Loops and Loop Control statements
Lecture 291 Control Flow Text.playground
Section 46: Functions
Lecture 292 Intro to Functions
Lecture 293 Function Parameters
Lecture 294 Return Statements
Lecture 295 Parameter Variations - Argument Labels
Lecture 296 Parameter Variations - Default Values
Lecture 297 Parameters Variations - InOut Parameters
Lecture 298 Parameter Variations - Variadic Parameters
Lecture 299 Returning Multiple Values Simultaneously
Lecture 300 Functions Text.playground
Section 47: Classes, Struct and Enums
Lecture 301 Topics List and Learning Objectives
Lecture 302 Intro to Classes
Lecture 303 Properties as fields - Add to Class Implementation
Lecture 304 Custom Getters and Setters
Lecture 305 Calculated Properties
Lecture 306 Variable Scope and Self
Lecture 307 Lazy and Static Variables
Lecture 308 Behaviour as Instance Methods and Class type Methods
Lecture 309 Behaviour and Instance Methods
Lecture 310 Class Type Methods
Lecture 311 Class Instances as Field Variables
Lecture 312 Inheritance, Subclassing and SuperClassing
Lecture 313 Overriding Initializers
Lecture 314 Overriding Properties
Lecture 315 Overriding Methods
Lecture 316 Structs Overview
Lecture 317 Enumerations
Lecture 318 Comparisons between Classes, Structs and Enums
Lecture 319 Classes, Structs, Enums Text.playground
Section 48: Practical MacOS BootCamps
Lecture 320 Introduction and UI Elements
Lecture 321 Calculator Setup and Tax Calculator
Lecture 322 Calculate Tax And Tip - Mammoth Interactive Source Code
Lecture 323 Tip Calculator and View Controller
Lecture 324 View Controller - Mammoth Interactive Source Code
Lecture 325 Constraints
Lecture 326 Constraints - Mammoth Interactive Source Code
Lecture 327 Constraints Code
Lecture 328 Refactor
Lecture 329 Refactor - Mammoth Interactive Source Code
Lecture 330 MacOsElements - Mammoth Interactive Source Code
Section 49: Data Mining With Python
Lecture 331 Data Wrangling and Section 1
Lecture 332 Project Files - Data Mining with Mammoth Interactive
Lecture 333 Project Files - Data Wrangling with Mammoth Interactive
Lecture 334 Data Mining Fundamentals
Lecture 335 Project Files - Data Mining fundamentals with Mammoth Interactive
Lecture 336 Framework Explained, Taming Big Bank with Data
Lecture 337 Project Files - Frameworks with Mammoth Interactive
Lecture 338 Mining and Storing Data
Lecture 339 Project Files - Mining and Storing with Mammoth Interactive
Lecture 340 NLP (Natural Language Processing)
Lecture 341 Project Files - NLP with Mammoth Interactive
Lecture 342 Summary Challenge
Lecture 343 Conclusion Files - Mammoth Interactive
Section 50: Introduction to Video Editing
Lecture 344 Introduction to the Course
Lecture 345 Installing Camtasia
Lecture 346 Exploring the Interface
Lecture 347 Camtasia Project Files
Section 51: Setting Up a Screen Recording
Lecture 348 Introduction and Tips for Recording
Lecture 349 Creating a Recording Account
Lecture 350 Full Screen vs Window Mode
Lecture 351 Setting the Recording Resolution
Lecture 352 Different Resolutions and their Uses
Lecture 353 Tips to Improve Recording Quality and Summary
Section 52: Camtasia Recording
Lecture 354 Introduction and Workflow
Lecture 355 Tools Options Menu
Lecture 356 Your First Recording
Lecture 357 Viewing your Test
Lecture 358 Challenge - VIDEO GAME NARRATION
Lecture 359 Mic Etiqutte
Lecture 360 Project - Recording Exercise
Lecture 361 Webcam, Telprompter, and Summary
Section 53: Camtasia Screen Layout
Lecture 362 Introduction and Tools Panel
Lecture 363 Canvas
Lecture 364 Zoom N Pan
Lecture 365 Annotations
Lecture 366 Yellow Snap Lines
Lecture 367 TimeLine Basics, Summary and Challenge
Section 54: Camtasia Editing
Lecture 368 Introduction and Importing Media
Lecture 369 Markers
Lecture 370 Split
Lecture 371 Working with Audio
Lecture 372 Clip Speed
Lecture 373 Locking and Disabling tracks
Lecture 374 Transitions
Lecture 375 Working with Images
Lecture 376 Voice Narration
Lecture 377 Noise Removal
Lecture 378 Smart Focus
Lecture 379 Summary and Challenge
Section 55: Advance Editing Introduction
Lecture 380 Advance Editing Introduction
Lecture 381 Zooming Multiple Tracks
Lecture 382 Easing
Lecture 383 Animations
Lecture 384 Behaviors
Lecture 385 Color Adjustment
Lecture 386 Clip Speed
Lecture 387 Remove a Color
Lecture 388 Device Frame
Lecture 389 Theme Manager
Lecture 390 Libraries
Lecture 391 Media and Summary
Section 56: Camtasia Resources and Tips
Lecture 392 Resources and Tips Introduction
Lecture 393 Masking
Lecture 394 Extending Frames
Lecture 395 Working with Video
Section 57: Exporting a Project for Youtube
Lecture 396 Exporting a Project for Youtube
Section 58: Code with C#
Lecture 397 Introduction to Course and Types
Lecture 398 Operator, Classes , and Additional Types
Lecture 399 Statements & Loops
Lecture 400 Arrays, Lists, and Strings
Lecture 401 Files, Directories, and Debugs
Lecture 402 Source code
Section 59: Learn to Code with R
Lecture 403 Intro to R
Lecture 404 Control Flow and Core Concepts
Lecture 405 Matrices, Dataframes, Lists and Data Manipulation
Lecture 406 GGplot and Intro to Machine learning
Lecture 407 Conclusion
Lecture 408 Source Code
Section 60: Advanced R
Lecture 409 Course Overview and Data Setup
Lecture 410 Source Code - Setting Up Data - Mammoth Interactive
Lecture 411 Functions in R
Lecture 412 Source Code - Functions - Mammoth Interactive
Lecture 413 Regression Model
Lecture 414 Source Code - Regression Models - Mammoth Interactive
Lecture 415 Regression Models Continued and Classification Models
Lecture 416 Source Code - Classification Models - Mammoth Interactive
Lecture 417 Classification Models Continued, RMark Down and Excel
Lecture 418 Source Code - RMarkDown And Excel - Mammoth Interactive
Lecture 419 Datasets - Mammoth Interactive
Section 61: Learn to Code with Java
Lecture 420 Introduction and setting up Android Studio
Lecture 421 Introduction - Encryption Source Code
Lecture 422 Setting up Continued
Lecture 423 Java Programming Fundamentals
Lecture 424 Source Code - Java Programming Fundamentals
Lecture 425 Additional Java fundamentals
Lecture 426 Source Code - Additional fundamentals
Lecture 427 Classes
Lecture 428 Source Code - Classes
Lecture 429 Please rate this course
Lecture 430 Bonus Lecture - Mammoth Interactive Deals
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.,Anyone who wants to learn the technology that is shaping how we interact with the world around us,Beginners who want to learn how to use data science to make graphs.,Experienced programmers who want to learn a 2D plotting library for Python.,Anyone who is interested in predictive modeling for handling the stock market, weather, and text
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