Learn To Master Python: From Beginner To Expert

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