Make Predictions With Python Machine Learning For Apps

Leverage TensorFlow models to build & improve apps! Use Google's deep learning framework w/ Java & AI. Beginner-friendly

What you'll learn
Master the basics: become an expert in Python and Java while learning core machine learning concepts
Machine learning goes mobile: learn how to incorporate machine learning models into Android apps
Optimize for intelligent apps: discover the TensorFlow mobile framework and build scientific analysis apps
Requirements
No experience required!
We will show you how to get all required programs for free
This course was recorded on a Mac, but you can use a PC
Description
Go through 3 ultimate levels of artificial intelligence for beginners!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.This course was funded by a wildly successful KickstarterUse 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 problemWe'll give you all necessary information to succeed from newbie to pro. We will install PyCharm 2017.2.3 and explore the interface. I will show you every step of the way. You will learn crucial Python 3.6.2 language fundamentals. Even if you have coding knowledge, going back to the basics is the key to success as a programmer. We will build and run Python projects. I teach through practical examples, follow-alongs, and over-the-shoulder tutorials. You won't need to go anywhere else.Then we will install Android Studio 3 and explore the interface. You will learn how to add a simulator and build simple User Interfaces (UIs). For coding, you will learn Java 8 language fundamentals. Java is a HUGE language that you must know, and I will tell you all about it. We will build and run Android projects directly in the course, and you will have solid examples to apply your knowledge immediately.Complete Image Recognition and Machine Learning for BeginnersWith this course I will help you understand what machine learning is and compare it to Artificial Intelligence (AI). Together we will discover applications of machine learning and where we use machine learning daily. Machine learning, neural networks, deep learning, and artificial intelligence are all around us, and they're not going away. I will show you how to get a grasp on this ever-growing technology in this course. We will explore different machine learning mechanisms and commonly used algorithms. These are popular and ones you should know.Next I'll teach you what TensorFlow 1.4.1 is and how it makes machine learning development easier. You will learn how to install TensorFlow and access its libraries through PyCharm. You'll understand the basic components of TensorFlow.Follow along with me to build a complete computational model. We'll train and test a model and use it for future predictions. I'll also show you how to build a linear regression model to fit a line through data. You'll learn to train and test the model, evaluate model accuracy, and predict values using the model.Stock Market, Weather & Text - Let's Go!

Overview

Section 1: Resources

Lecture 1 Resources

Section 2: Intro to Android Studio

Lecture 2 Intro to Android and Project Outline

Lecture 3 Downloading and Installing Android Studio

Lecture 4 Exploring Interface

Lecture 5 Setting up Emulator and Running Project

Section 3: Intro to Java

Lecture 6 Java Language Basics

Lecture 7 Variable Types

Lecture 8 Operations on Variables

Lecture 9 Arrays and Lists

Lecture 10 Array and List Operations

Lecture 11 If Statements and Switch Statements

Lecture 12 While Loops

Lecture 13 For Loops

Lecture 14 Functions

Lecture 15 Parameters and Return Values

Lecture 16 Classes and Objects

Lecture 17 Superclass and Subclasses

Lecture 18 Static Variables and Axis Modifiers

Section 4: ––––––-App Development––––––-

Lecture 19 Android App Development

Lecture 20 Building Basic User Interface

Lecture 21 Connecting UI to Backend

Lecture 22 Implementing Backend and Tidying UI

Section 5: Machine Learning Concepts

Lecture 23 ML Concepts Introduction

Lecture 24 Intro to PyCharm and Project Outline

Lecture 25 How to Install PyCharm and Python

Lecture 26 Let's Explore PyCharm

Lecture 27 (Files) Source Code

Section 6: Python Language Basics

Lecture 28 Variables

Lecture 29 Variable Operations and Conversions

Lecture 30 Collection Types

Lecture 31 Operations on Collections

Lecture 32 Control Flow: If Statements

Lecture 33 While and For Loops

Lecture 34 Functions

Lecture 35 Classes and Objects

Lecture 36 (Files) Source Code

Section 7: TensorFlow

Lecture 37 TensorFlow Introduction

Lecture 38 Project Outline

Lecture 39 How to Import TensorFlow to PyCharm

Lecture 40 Constant Nodes and Sessions

Lecture 41 Variable Nodes

Lecture 42 Placeholder Nodes

Lecture 43 Operation Nodes

Lecture 44 Loss, Optimizers, and Training

Lecture 45 Building a Linear Regression Model

Lecture 46 (Files) Source Code

Section 8: ––––––-Machine Learning in Android Studio Projects––––––-

Lecture 47 Introduction to ML for Android

Section 9: TensorFlow Estimator

Lecture 48 TensorFlow Estimator Introduction

Lecture 49 Project Outline

Lecture 50 Setting up Prebuilt Estimator Model

Lecture 51 Evaluating and Predicting with Model

Lecture 52 Building Custom Estimator Function

Lecture 53 Testing Custom Estimator Function

Lecture 54 Summary and Model Comparison

Lecture 55 (Files) Source Code

Section 10: Importing Android Machine Learning Model

Lecture 56 Intro & Demo: ML Model Import

Lecture 57 Project Outline

Lecture 58 Formatting and Saving Model

Lecture 59 Saving Optimized Graph File

Lecture 60 Starting Android Project

Lecture 61 Building UI

Lecture 62 Implementing Inference Functionality

Lecture 63 Testing and Error Handling

Lecture 64 (Files) Source Code

Section 11: Simple MNIST

Lecture 65 Intro & Demo: Simple MNIST

Lecture 66 Project Outline and Intro to MNIST Data

Lecture 67 Building Computational Graph

Lecture 68 Training and Testing Model

Lecture 69 Saving Graph for Android Import

Lecture 70 Setting up Android Studio Project

Lecture 71 Building User Interface

Lecture 72 Loading Digit Images

Lecture 73 Formatting Image Data

Lecture 74 Making Prediction Using Model

Lecture 75 Displaying Results and Summary

Lecture 76 (Files) Source Code

Section 12: MNIST with Estimator

Lecture 77 MNIST With Estimator Introduction

Lecture 78 Project Outline

Lecture 79 Building Custom Estimator Function

Lecture 80 Training & Testing Input Functions

Lecture 81 Predicting Using Model & Comparisons

Lecture 82 (Files) Source Code

Section 13: ––––––-Build Image Recognition Apps––––––-

Lecture 83 Introduction to Image Recognition Apps

Section 14: Weather Prediction

Lecture 84 Intro and Demo: Weather Prediction

Lecture 85 Project Outline

Lecture 86 Retrieving Data

Lecture 87 Formatting Datasets

Lecture 88 Building Computational Graphs

Lecture 89 Writing, Training, Testing, & Evaluating

Lecture 90 Training, Testing, and Freezing Model

Lecture 91 Setting up Android Project

Lecture 92 Building UI

Lecture 93 Build App Backend and Project Summary

Lecture 94 (Files) Source Code

Section 15: Text Prediction

Lecture 95 Intro and Demo: Text Prediction

Lecture 96 Project Outline

Lecture 97 Processing Text Data

Lecture 98 Building Datasets and Model Builder

Lecture 99 Building Computational Graph

Lecture 100 Writing, Training, and Testing Code

Lecture 101 Training, Testing, and Freezing Graph

Lecture 102 Setting up Android Project

Lecture 103 Setting up UI

Lecture 104 Setting up Vocab Dictionary

Lecture 105 Formatting Input and Running Through Model

Lecture 106 (Files) Source Code

Section 16: Stock Market Prediction

Lecture 107 Intro & Demo: Stock Market Prediction

Lecture 108 Project Outline

Lecture 109 Retrieving Data via RESTful API Call

Lecture 110 Parsing JSON Data PyCharm Style

Lecture 111 Formatting Data

Lecture 112 Building the Model

Lecture 113 Training and Testing Model

Lecture 114 Freezing Graph

Lecture 115 Setting up Android Project

Lecture 116 Building UI

Lecture 117 Requesting Data Via AsyncTask

Lecture 118 Parsing JSON Data Android Style

Lecture 119 Running Inference and Displaying Results

Lecture 120 (Files) Source Code

Section 17: ––––––-Bonus––––––-

Lecture 121 Please rate this course

Lecture 122 Bonus Lecture: Community Newsletter

People who want to learn machine learning concepts through practical projects with PyCharm, Python, Android Studio, Java, and TensorFlow,Anyone who wants to learn the technology that is shaping how we interact with the world around us,Anyone who is interested in predictive modeling for handling the stock market, weather, and text