Data Science And Machine Learning With Python And Tensorflow

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.