Artificial Intelligence A-Z™: Learn How To Build An AI

Artificial Intelligence A-Z™: Learn How To Build An AI

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 17 Hours | 2.45 GB

Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!

Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Covering:

  • How to start building AI with no previous coding experience using Python
  • How to merge AI with OpenAI Gym to learn as effectively as possible
  • How to optimize your AI to reach its maximum potential in the real world

Here is what you will get with this course:

1. Complete beginner to expert AI skills – Learn to code self-improving AI for a range of purposes. In fact, we code together with you. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.

2. Code templates – Plus, you’ll get downloadable Python code templates for every AI you build in the course. This makes building truly unique AI as simple as changing a few lines of code. If you unleash your imagination, the potential is unlimited.

3. Intuition Tutorials – Where most courses simply bombard you with dense theory and set you on your way, we believe in developing a deep understanding for not only what you’re doing, but why you’re doing it. That’s why we don’t throw complex mathematics at you, but focus on building up your intuition in coding AI making for infinitely better results down the line.

4. Real-world solutions – You’ll achieve your goal in not only 1 game but in 3. Each module is comprised of varying structures and difficulties, meaning you’ll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory “test and forget” like most other courses. Practice truly does make perfect.

5. In-course support – We’re fully committed to making this the most accessible and results-driven AI course on the planet. This requires us to be there when you need our help. That’s why we’ve put together a team of professional Data Scientists to support you in your journey, meaning you’ll get a response from us within 48 hours maximum.

What Will I Learn?

  • Build an AI
  • Understand the theory behind Artificial Intelligence
  • Make a virtual Self Driving Car
  • Make an AI to beat games
  • Solve Real World Problems with AI
  • Master the State of the Art AI models
  • Q-Learning
  • Deep Q-Learning
  • Deep Convolutional Q-Learning
  • A3C
Table of Contents

Introduction Getting Started
1 Introduction to the course Instructor

Setup Your Computer for Development – Windows PC MAC
2 Setting Up Java – Windows PC
3 Download and Install Eclipse – Windows PC
4 How to Setup JAVA_HOME – Windows 10
5 Lets Run our First Hello World
6 IMPORTANT Download Source Code Files Eclipse Project Files
7 Installing Eclipse Setting Up Java – MAC

Java Introduction
8 Whats Java
9 The Way Java Works Compilation Process
10 Introduction to Variable and How To Declare Them
11 Integers and Concatenation
12 String Integer Float Chars Doubles Booleans
13 Handling Syntax Errors
14 Java Operators – Addition
15 Java Operators Addition Multiplication Division…
16 App 3 Lets Write a Program to Convert Meters to Feet
17 App 4 Improved Meters to Feet Converter App

Decision Making – If Else Logical Operators Loops
18 If Statements and Conditional Operators
19 Logical Operators – AND OR NOT
20 Loop Controls – for loop while loop do while loops

Introduction to Classes in Java – Methods Inheritance Data Hiding
21 Methods
22 Methods Return Types
23 Whats a Class – Introduction to Classes
24 Inheritance in Java
25 Controlling Access to Instance Variables Properties
26 Constructors in Java
27 Setters and Getters in Java
28 Overloading Constructors
29 The Java Class Library
30 Static Keyword in Java and the Math Class
31 Object – The Ultimate Superclass
32 Error Handling Exceptions in Java
33 Multiple Catch Block and Finally Block

Introduction to Arrays and Advanced Data Structures -Data storage With ArrayList
34 Whats an Array Creating Arrays in Java
35 Arrays – Continuation
36 Introduction to ArrayLists
37 Java HashMaps
38 Sorting an Array

Java – Introduction to IO Classes – Input and Output Streams
39 Introduction to the IO Classes in Java
40 IO – Read Text a TextFile
41 IO Write To File
42 Java – Buffer Reader

Introduction to Swing AWT – Abstract Windowing Toolkit
43 Whats Swing ant AWT
44 Creating Windows and Frames – Swing Demo
45 Java Swing JLabel
46 Layout Manager and Buttons – Java Swing JButton Class
47 Java Abstract Classes
48 Interface Classes in Java
49 Add EventListener to a Button
50 JTextField
51 Lets Build a Java Swing Application – Flash Card – Part 1
52 Java Swing Application – Flash Card – Part 2
53 Java Swing Application – Flash Card – Part 3
54 Java Swing Application – Flash Card – Part 4
55 Lets Build a Java Swing Application – FlashCard – 5
56 Lets Build a Java Swing Application – FlashCard – Final

Welcome to the course
1 Why AI
2 Introduction
3 Where to get the Materials
4 Some Additional Resources

Part 0 – Fundamentals Of Reinforcement Learning
5 Welcome to Part 0 – Fundamentals of Reinforcement Learning

Q-Learning Intuition
6 Plan of Attack
7 What is reinforcement learning
8 The Bellman Equation
9 The Plan
10 Markov Decision Process
11 Policy vs Plan
12 Adding a Living Penalty
13 Q-Learning Intuition
14 Temporal Difference
15 Q-Learning Visualization

Part 1 – Self-Driving Car (Deep Q-Learning)
16 Welcome to Part 1 – Self-Driving Car (Deep Q-Learning)

Deep Q-Learning Intuition
17 Plan of Attack
18 Deep Q-Learning Intuition – Learning
19 Deep Q-Learning Intuition – Acting
20 Experience Replay
21 Action Selection Policies

Installation for Part 1
22 Plan of Attack (Practical Tutorials)
23 Where to get the Materials
24 Windows Option 1 End-to-End installation steps
25 Windows Option 2 – Part A Installing Ubuntu on Windows
26 Windows Option 2 – Part B Installing PyTorch and Kivy on your Ubuntu VM
27 Mac or Linux Installing Anaconda
28 Mac or Linux Installing PyTorch and Kivy
29 Kivy Installation Walk Through Mac
30 Kivy Installation Walk Through Linux
31 Getting Started

Creating the environment
32 Self Driving Car – Step 1
33 Self Driving Car – Step 2

Building an AI
34 Self Driving Car – Step 3
35 Self Driving Car – Step 4
36 Self Driving Car – Step 5
37 Self Driving Car – Step 6
38 Self Driving Car – Step 7
39 Self Driving Car – Step 8
40 Self Driving Car – Step 9
41 Self Driving Car – Step 10
42 Self Driving Car – Step 11
43 Self Driving Car – Step 12
44 Self Driving Car – Step 13
45 Self Driving Car – Step 14
46 Self Driving Car – Step 15
47 Self Driving Car – Step 16

Playing with the AI
48 Self Driving Car – Level 1
49 Self Driving Car – Level 2
50 Self Driving Car – Level 3
51 Self Driving Car – Level 4
52 Challenge Solutions

Part 2 – Doom (Deep Convolutional Q-Learning)
53 Welcome to Part 2 – Doom (Deep Convolutional Q-Learning)

Deep Convolutional Q-Learning Intuition
54 Plan of Attack
55 Deep Convolutional Q-Learning Intuition
56 Eligibility Trace

Installation for Part 2
57 Where to get the Materials
58 Installing Open AI Gym and ppaquette
59 Installing Open AI Gym Walk Through (Mac Version)
60 Installing Open AI Gym Walk Through (Ubuntu Version)

Building an AI
61 Doom – Step 1
62 Doom – Step 2
63 Doom – Step 3
64 Doom – Step 4
65 Doom – Step 5
66 Doom – Step 6
67 Doom – Step 7
68 Doom – Step 8
69 Doom – Step 9
70 Doom – Step 10
71 Doom – Step 11
72 Doom – Step 12
73 Doom – Step 13
74 Doom – Step 14
75 Doom – Step 15
76 Doom – Step 16
77 Doom – Step 17

Playing with the AI
78 Watching our AI play Doom

Part 3 – Breakout (A3C)
79 Welcome to Part 3 – Breakout (A3C)

A3C Intuition
80 Plan of Attack
81 The three As in A3C
82 Actor-Critic
83 Asynchronous
84 Advantage
85 LSTM Layer

Installation for Part 3
86 Installing OpenCV

Building an AI
87 Breakout – Step 1
88 Breakout – Step 2
89 Breakout – Step 3
90 Breakout – Step 4
91 Breakout – Step 5
92 Breakout – Step 6
93 Breakout – Step 7
94 Breakout – Step 8
95 Breakout – Step 9
96 Breakout – Step 10
97 Breakout – Step 11
98 Breakout – Step 12
99 Breakout – Step 13
100 Breakout – Step 14
101 Breakout – Step 15

Annex 1 Artificial Neural Networks
102 What is Deep Learning
103 Plan of Attack
104 The Neuron
105 The Activation Function
106 How do Neural Networks work
107 How do Neural Networks learn
108 Gradient Descent
109 Stochastic Gradient Descent
110 Backpropagation

Annex 2 Convolutional Neural Networks
111 Plan of Attack
112 What are convolutional neural networks
113 Step 1 – Convolution Operation
114 Step 1(b) – ReLU Layer
115 Step 2 – Pooling
116 Step 3 – Flattening
117 Step 4 – Full Connection
118 Summary
119 Softmax Cross-Entropy

Bonus Lectures
120 YOUR SPECIAL BONUS