What you'll learn

Build 7 different AIs for 7 different applications
Understand the theory behind Artificial Intelligence
Master the State of the Art AI models
Solve Real World Problems with AI
Q-Learning
Deep Q-Learning
Deep Convolutional Q-Learning
A3C (Asynchronous Advantage Actor-Critic)
PPO (Proximal Policy Optimization)
SAC (Soft Actor-Critic)
LLMs
Transformers
Low-Rank Adaptation (LoRA) and Quantization (QLoRA)
NLP techniques for Chatbots: Tokenization and Padding
Fine-Tuning an LLM with Knowledge Augmentation
As Extras: DDPG, Full World Model, Evolution Strategies & Genetic Algorithms

Requirements

High School Maths
Basic Python knowledge

Description

Welcome to Artificial Intelligence A-Z!Learn key AI concepts with intuition lectures to get you quickly up to speed with all things AI and practice them by building 7 different AIs:Build an AI with a Q-Learning model and train it to optimize warehouse flows in a Process Optimization case study.Build an AI with a Deep Q-Learning model and train it to land on the moon.Build an AI with a Deep Convolutional Q-Learning model and train it to play the game of Pac-Man.Build an AI with an A3C (Asynchronous Advantage Actor-Critic) model and train it to fight Kung Fu.Build an AI with a PPO (Proximal Policy Optimization) model and train it for a Self-Driving Car.Build an AI with a SAC (Soft Actor-Critic) model and train it for a Self-Driving Car.Build an AI by fine-tuning a powerful pre-trained LLM (Llama 2 by Meta) with Hugging Face and re-train it to chat with you about medical terms. Simply put, we build here an AI Doctor Chatbot.But that's not all... Once you complete the course, you will get 3 extra AIs: DDPG, Full World Model, and Evolution Strategies & Genetic Algorithms. We build these AIs with ChatGPT for a Self-Driving Car and a Humanoid application. For each of these extra AIs you will get a long video lecture explaining the implementation, a mini PDF, and the Python code.Besides, you will get a free 3-hour extra course on Generative AI and LLMs with Cloud Computing as a Prize for completing the course.And last but not least, here is what you will get with this course:1. Complete beginner to expert AI skills â 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. Hassle-Free Coding and Code templates â We will build all our AIs in Google Colab, which means that we will have absolutely NO hassle installing libraries or packages because everything is already pre-installed in Google Colab notebooks. Plus, youâll get downloadable Python code templates (in .py and .ipynb) 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 â 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 youâre doing, but why youâre doing it. Thatâs why we donât throw complex mathematics at you, but focus on building up your intuition in AI for much better results down the line.4. Real-world solutions â Youâll achieve your goal in not only one AI model but in 5. Each module is comprised of varying structures and difficulties, meaning youâll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory âtest and forgetâ like most other courses. Practice truly does make perfect.5. In-course support â Weâre 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. Thatâs why weâve put together a team of professional Data Scientists to support you in your journey, meaning youâll get a response from us within 48 hours maximum.So, are you ready to embrace the fascinating world of AI?Come join us, never stop learning, and enjoy AI!

Overview

Section 1: Welcome to the course!
Lecture 1 Welcome Challenge!
Lecture 2 Why AI?
Lecture 3 Hello from Hadelin
Lecture 4 Quick Win: Build a ChatBot app that speaks like Master Yoda
Lecture 5 Course Structure, Codes, Additional Reading, and PDF
Lecture 6 EXTRA: Use ChatGPT to Build AI More Efficiently
Section 2: ---------- Part 0 - Fundamentals Of Reinforcement Learning ----------
Lecture 7 Welcome to Part 0 - Fundamentals of Reinforcement Learning
Section 3: Q-Learning Intuition
Lecture 8 Plan of Attack
Lecture 9 What is reinforcement learning?
Lecture 10 The Bellman Equation
Lecture 11 The "Plan"
Lecture 12 Markov Decision Process
Lecture 13 Policy vs Plan
Lecture 14 Adding a "Living Penalty"
Lecture 15 Q-Learning Intuition
Lecture 16 Temporal Difference
Section 4: Q-Learning Implementation
Lecture 17 A Q-Learning Implementation for Process Optimization
Section 5: ---------- Part 1 - Deep Q-Learning ----------
Lecture 18 Welcome to Part 1 - Deep Q-Learning
Section 6: Deep Q-Learning Intuition
Lecture 19 Plan of Attack
Lecture 20 Deep Q-Learning Intuition - Learning
Lecture 21 Deep Q-Learning Intuition - Acting
Lecture 22 Experience Replay
Lecture 23 Action Selection Policies
Section 7: Deep Q-Learning Implementation
Lecture 24 Welcome to the Practical Activity of Part 1!
Lecture 25 Get the Codes here
Lecture 26 Deep Q-Learning Implementation - Step 1
Lecture 27 Deep Q-Learning Implementation - Step 2
Lecture 28 Deep Q-Learning Implementation - Step 3
Lecture 29 Deep Q-Learning Implementation - Step 4
Lecture 30 Deep Q-Learning Implementation - Step 5
Lecture 31 Deep Q-Learning Implementation - Step 6
Lecture 32 Deep Q-Learning Implementation - Step 7
Lecture 33 Deep Q-Learning Implementation - Step 8
Lecture 34 Deep Q-Learning Implementation - Step 9
Lecture 35 Deep Q-Learning Implementation - Step 10
Lecture 36 Deep Q-Learning Implementation - Step 11
Lecture 37 Deep Q-Learning Implementation - Step 12
Lecture 38 Deep Q-Learning Implementation - Step 13
Lecture 39 Deep Q-Learning Implementation - Step 14
Lecture 40 Deep Q-Learning Implementation - Step 15
Lecture 41 Deep Q-Learning Implementation - Step 16
Lecture 42 Deep Q-Learning Implementation - Step 17
Lecture 43 Deep Q-Learning Implementation - Step 18
Lecture 44 Deep Q-Learning Implementation - Step 19
Lecture 45 Deep Q-Learning Implementation - Step 20
Section 8: ---------- Part 2 - Deep Convolutional Q-Learning ----------
Lecture 46 Welcome to Part 2 - Deep Convolutional Q-Learning
Section 9: Deep Convolutional Q-Learning Intuition
Lecture 47 Plan of Attack
Lecture 48 Deep Convolutional Q-Learning Intuition
Lecture 49 Eligibility Trace
Section 10: Deep Convolutional Q-Learning Implementation
Lecture 50 Welcome to the Practical Activity of Part 2!
Lecture 51 Get the Codes here
Lecture 52 Deep Convolutional Q-Learning Implementation - Step 1
Lecture 53 Deep Convolutional Q-Learning Implementation - Step 2
Lecture 54 Deep Convolutional Q-Learning Implementation - Step 3
Lecture 55 Deep Convolutional Q-Learning Implementation - Step 4
Lecture 56 Deep Convolutional Q-Learning Implementation - Step 5
Lecture 57 Deep Convolutional Q-Learning Implementation - Step 6
Lecture 58 Deep Convolutional Q-Learning Implementation - Step 7
Lecture 59 Deep Convolutional Q-Learning Implementation - Step 8
Lecture 60 Deep Convolutional Q-Learning Implementation - Step 9
Lecture 61 Deep Convolutional Q-Learning Implementation - Step 10
Lecture 62 Deep Convolutional Q-Learning Implementation - Step 11
Lecture 63 Deep Convolutional Q-Learning Implementation - Step 12
Lecture 64 Deep Convolutional Q-Learning Implementation - Step 13
Section 11: ---------- Part 3 - A3C ----------
Lecture 65 Welcome to Part 3 - A3C
Section 12: A3C Intuition
Lecture 66 Plan of Attack
Lecture 67 The three A's in A3C
Lecture 68 Actor-Critic
Lecture 69 Asynchronous
Lecture 70 Advantage
Lecture 71 LSTM Layer
Section 13: A3C Implementation
Lecture 72 Welcome to the Practical Activity of Part 3!
Lecture 73 Get the Codes here
Lecture 74 A3C Implementation - Step 1
Lecture 75 A3C Implementation - Step 2
Lecture 76 A3C Implementation - Step 3
Lecture 77 A3C Implementation - Step 4
Lecture 78 A3C Implementation - Step 5
Lecture 79 A3C Implementation - Step 6
Lecture 80 A3C Implementation - Step 7
Lecture 81 A3C Implementation - Step 8
Lecture 82 A3C Implementation - Step 9
Lecture 83 A3C Implementation - Step 10
Lecture 84 A3C Implementation - Step 11
Lecture 85 A3C Implementation - Step 12
Lecture 86 A3C Implementation - Step 13
Lecture 87 A3C Implementation - Step 14
Lecture 88 A3C Implementation - Step 15
Section 14: ---------- Part 4 - PPO and SAC ----------
Lecture 89 Welcome to the Practical Activity of Part 4!
Lecture 90 Build and Train the PPO and SAC models for a Self-Driving Car! Theory included.
Section 15: ---------- Part 5 - Intro to Large Language Models (LLMs) ----------
Lecture 91 Welcome to Part 5 - Intro to Large Language Models (LLMs)
Section 16: LLMs Intuition
Lecture 92 Introduction to LLMs
Lecture 93 Ingredients of an LLM
Lecture 94 Who invented LLMs?
Lecture 95 How LLMs generate text
Lecture 96 Inside an LLM - Under the Hood
Lecture 97 LLM Parameters
Lecture 98 LLM Context Window
Lecture 99 Fine-Tuning LLMs
Section 17: LLMs Implementation
Lecture 100 Welcome to the Practical Activity of Part 5!
Lecture 101 Get the Codes here
Lecture 102 Fine-Tuning LLMs with Hugging Face - Step 1
Lecture 103 Fine-Tuning LLMs with Hugging Face - Step 2
Lecture 104 Fine-Tuning LLMs with Hugging Face - Step 3
Lecture 105 Fine-Tuning LLMs with Hugging Face - Step 4
Lecture 106 Fine-Tuning LLMs with Hugging Face - Step 5
Lecture 107 Fine-Tuning LLMs with Hugging Face - Step 6
Lecture 108 Fine-Tuning LLMs with Hugging Face - Step 7
Section 18: THANK YOU
Lecture 109 THANK YOU Video
Section 19: Annex 1: Artificial Neural Networks
Lecture 110 What is Deep Learning?
Lecture 111 Plan of Attack
Lecture 112 The Neuron
Lecture 113 The Activation Function
Lecture 114 How do Neural Networks work?
Lecture 115 How do Neural Networks learn?
Lecture 116 Gradient Descent
Lecture 117 Stochastic Gradient Descent
Lecture 118 Backpropagation
Section 20: Annex 2: Convolutional Neural Networks
Lecture 119 Plan of Attack
Lecture 120 What are convolutional neural networks?
Lecture 121 Step 1 - Convolution Operation
Lecture 122 Step 1(b) - ReLU Layer
Lecture 123 Step 2 - Pooling
Lecture 124 Step 3 - Flattening
Lecture 125 Step 4 - Full Connection
Lecture 126 Summary
Lecture 127 Softmax & Cross-Entropy
Section 21: Congratulations!! Don't forget your Prize :)
Lecture 128 Huge Congrats for completing the challenge!
Lecture 129 Bonus: How To UNLOCK Top Salaries (Live Training)
Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning


Homepage:

https://www.udemy.com/course/artificial-intelligence-az/