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Artificial Intelligence A-Z 2024: Build 7 AI + LLM & ChatGPT

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Artificial Intelligence A-Z 2024: Build 7 AI + LLM & ChatGPT, available at $199.99, has an average rating of 4.5, with 135 lectures, 1 quizzes, based on 40636 reviews, and has 287191 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning It is particularly useful for Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning.

Enroll now: Artificial Intelligence A-Z 2024: Build 7 AI + LLM & ChatGPT

Summary

Title: Artificial Intelligence A-Z 2024: Build 7 AI + LLM & ChatGPT

Price: $199.99

Average Rating: 4.5

Number of Lectures: 135

Number of Quizzes: 1

Number of Published Lectures: 123

Number of Published Quizzes: 1

Number of Curriculum Items: 136

Number of Published Curriculum Objects: 124

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning

Target Audiences

  • Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning

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:

  1. Build an AI with a Q-Learning model and train it to optimize warehouse flows in a Process Optimization case study.

  2. Build an AI with a Deep Q-Learning model and train it to land on the moon.

  3. Build an AI with a Deep Convolutional Q-Learning model and train it to play the game of Pac-Man.

  4. Build an AI with an A3C (Asynchronous Advantage Actor-Critic) model and train it to fight Kung Fu.

  5. Build an AI with a PPO (Proximal Policy Optimization) model and train it for a Self-Driving Car.

  6. Build an AI with a SAC (Soft Actor-Critic) model and train it for a Self-Driving Car.

  7. 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 – 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 – 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, you’ll 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 – 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 AI for much better results down the line.

4. Real-world solutions – You’ll achieve your goal in not only one AI model but in 5. 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.

So, are you ready to embrace the fascinating world of AI?

Come join us, never stop learning, and enjoy AI!

Course Curriculum

Chapter 1: Welcome to the course!

Lecture 1: Welcome Challenge!

Lecture 2: Why AI?

Lecture 3: Course Structure

Lecture 4: Get the PDF Handbook here

Lecture 5: EXTRA: Use ChatGPT to Build AI More Efficiently

Chapter 2: ———- Part 0 – Fundamentals Of Reinforcement Learning ———-

Lecture 1: Welcome to Part 0 – Fundamentals of Reinforcement Learning

Chapter 3: Q-Learning Intuition

Lecture 1: Plan of Attack

Lecture 2: What is reinforcement learning?

Lecture 3: The Bellman Equation

Lecture 4: The "Plan"

Lecture 5: Markov Decision Process

Lecture 6: Policy vs Plan

Lecture 7: Adding a "Living Penalty"

Lecture 8: Q-Learning Intuition

Lecture 9: Temporal Difference

Chapter 4: Q-Learning Implementation

Lecture 1: A Q-Learning Implementation for Process Optimization

Chapter 5: ———- Part 1 – Deep Q-Learning ———-

Lecture 1: Welcome to Part 1 – Deep Q-Learning

Chapter 6: Deep Q-Learning Intuition

Lecture 1: Plan of Attack

Lecture 2: Deep Q-Learning Intuition – Learning

Lecture 3: Deep Q-Learning Intuition – Acting

Lecture 4: Experience Replay

Lecture 5: Action Selection Policies

Chapter 7: Deep Q-Learning Implementation

Lecture 1: Get the Codes here

Lecture 2: Deep Q-Learning Implementation – Step 1

Lecture 3: Deep Q-Learning Implementation – Step 2

Lecture 4: Deep Q-Learning Implementation – Step 3

Lecture 5: Deep Q-Learning Implementation – Step 4

Lecture 6: Deep Q-Learning Implementation – Step 5

Lecture 7: Deep Q-Learning Implementation – Step 6

Lecture 8: Deep Q-Learning Implementation – Step 7

Lecture 9: Deep Q-Learning Implementation – Step 8

Lecture 10: Deep Q-Learning Implementation – Step 9

Lecture 11: Deep Q-Learning Implementation – Step 10

Lecture 12: Deep Q-Learning Implementation – Step 11

Lecture 13: Deep Q-Learning Implementation – Step 12

Lecture 14: Deep Q-Learning Implementation – Step 13

Lecture 15: Deep Q-Learning Implementation – Step 14

Lecture 16: Deep Q-Learning Implementation – Step 15

Lecture 17: Deep Q-Learning Implementation – Step 16

Lecture 18: Deep Q-Learning Implementation – Step 17

Lecture 19: Deep Q-Learning Implementation – Step 18

Lecture 20: Deep Q-Learning Implementation – Step 19

Lecture 21: Deep Q-Learning Implementation – Step 20

Chapter 8: ———- Part 2 – Deep Convolutional Q-Learning ———-

Lecture 1: Welcome to Part 2 – Deep Convolutional Q-Learning

Chapter 9: Deep Convolutional Q-Learning Intuition

Lecture 1: Plan of Attack

Lecture 2: Deep Convolutional Q-Learning Intuition

Lecture 3: Eligibility Trace

Chapter 10: Deep Convolutional Q-Learning Implementation

Lecture 1: Get the Codes here

Lecture 2: Deep Convolutional Q-Learning Implementation – Step 1

Lecture 3: Deep Convolutional Q-Learning Implementation – Step 2

Lecture 4: Deep Convolutional Q-Learning Implementation – Step 3

Lecture 5: Deep Convolutional Q-Learning Implementation – Step 4

Lecture 6: Deep Convolutional Q-Learning Implementation – Step 5

Lecture 7: Deep Convolutional Q-Learning Implementation – Step 6

Lecture 8: Deep Convolutional Q-Learning Implementation – Step 7

Lecture 9: Deep Convolutional Q-Learning Implementation – Step 8

Lecture 10: Deep Convolutional Q-Learning Implementation – Step 9

Lecture 11: Deep Convolutional Q-Learning Implementation – Step 10

Lecture 12: Deep Convolutional Q-Learning Implementation – Step 11

Lecture 13: Deep Convolutional Q-Learning Implementation – Step 12

Lecture 14: Deep Convolutional Q-Learning Implementation – Step 13

Chapter 11: ———- Part 3 – A3C ———-

Lecture 1: Welcome to Part 3 – A3C

Chapter 12: A3C Intuition

Lecture 1: Plan of Attack

Lecture 2: The three A's in A3C

Lecture 3: Actor-Critic

Lecture 4: Asynchronous

Lecture 5: Advantage

Lecture 6: LSTM Layer

Chapter 13: A3C Implementation

Lecture 1: Get the Codes here

Lecture 2: A3C Implementation – Step 1

Lecture 3: A3C Implementation – Step 2

Lecture 4: A3C Implementation – Step 3

Lecture 5: A3C Implementation – Step 4

Lecture 6: A3C Implementation – Step 5

Lecture 7: A3C Implementation – Step 6

Lecture 8: A3C Implementation – Step 7

Lecture 9: A3C Implementation – Step 8

Lecture 10: A3C Implementation – Step 9

Lecture 11: A3C Implementation – Step 10

Lecture 12: A3C Implementation – Step 11

Lecture 13: A3C Implementation – Step 12

Lecture 14: A3C Implementation – Step 13

Lecture 15: A3C Implementation – Step 14

Lecture 16: A3C Implementation – Step 15

Chapter 14: ———- Part 4 – PPO and SAC ———-

Lecture 1: Build and Train the PPO and SAC models for a Self-Driving Car! Theory included.

Instructors

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  • 1 stars: 800 votes
  • 2 stars: 960 votes
  • 3 stars: 4461 votes
  • 4 stars: 14247 votes
  • 5 stars: 20168 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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