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AI A-Z [2026] Agentic AI, Gen AI, Prompt Engineering and RL
Last updated 6/2026
Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, Luka Anicin, Ligency
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 163 Lectures ( 17h 22m ) | Size: 7.4 GB
Combine the power of Agentic AI, Generative AI, Prompt Engineering and Deep RL to build powerful AIs with AWS and Python
What you'll learn
⚡ Understand the theory behind Artificial Intelligence
⚡ Build 12 different AIs for 12 different applications
⚡ Master the State of the Art AI models
⚡ Solve Real World Problems with AI
⚡ Prompt Engineering
⚡ Generative AI
⚡ Image Generation
⚡ Foundation Models Fine-Tuning
⚡ Retrieval-Augmented Generation (RAG)
⚡ Agentic 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)
⚡ Responsible AI
Requirements
❗ High School Maths
❗ Basic Python knowledge
Description
Welcome to Artificial Intelligence A-Z!
This course is structured in 10 parts
✨Part 1 - Prompt Engineering: Prompt Engineering & Prompt Templates, Prompt Engineering Techniques, The 4 Elements of a (good) prompt, Inference Parameters
✨Part 2 - Generative AI: Fundamentals of Generative AI, Image Generation, Foundation Models Overview, Foundation Models Lifecycle, Data Selection, Foundation Models Selection, Training vs. Inference, Context Window, Tokens and Embeddings, Transformers, Foundation Models Training, Foundation Models Fine-Tuning, Foundation Models Evaluation, Retrieval-Augmented Generation (RAG) for Cooking Assistance
✨Part 3 - Agentic AI:AI Agents, Building a Cloud-powered AI Agent for Business Assistance
✨Part 4 - Fundamentals of Reinforcement Learning: Q-Learning Intuition, Q-Learning Implementation
✨Part 5 - Deep Q-Learning: Deep Q-Learning Intuition, Deep Q-Learning Implementation for Moon Landing
✨Part 6 - Deep Convolutional Q-Learning: Deep Convolutional Q-Learning Intuition, Deep Convolutional Q-Learning Implementation for Pac-Man
✨Part 7 - A3C: A3C Intuition, A3C Implementation for Kung Fu
✨Part 8 - PPO and SAC: Proximal Policy Optimization, Soft Actor-Critic, Build and Train the PPO & SAC models for Self-Driving Cars
✨Part 9 - LLMs: The Ingredients of an LLM, Who invented LLMs, How LLMs generate text, Understand what's inside an LLM, The LLM Parameters, The LLM Context Window, How to Fine-Tune LLMs for Medical Assistance
✨Part 10 - Responsible AI:Features of Responsible AI, Guardrails in Generative AI, Legal Risks of Generative AI, AWS Tools for Responsible AI, Amazon SageMaker Clarify and Monitor, Amazon Augmented AI[Amazon A2I], Interpretability vs. Explainability, SageMaker Model Cards
All along this journey, you will learn key AI concepts with intuition lectures to get you quickly up to speed with all things AI and practice them by building12 different AIs
✨ Build aChatBot App that speaks like Master Yoda in 5 Minutes.
✨ Build a Movie Script Generator by leveraging advancedPrompt Engineering.
✨ Build YourCustom LLM with Amazon Bedrock, Databricks, and Hugging Face.
✨ Build aRAG-powered Generative AI application with Amazon Bedrock and Knowledge Bases.
✨ Build anAI Agent with a Foundation Model (LLM) for business assistance, all powered by the Cloud.
✨ Build an AI with aQ-Learning model and train it to optimize warehouse flows in a Process Optimization case study.
✨ Build an AI with aDeep Q-Learning model and train it to land on the moon.
✨ Build an AI with aDeep Convolutional Q-Learning model and train it to play the game of Pac-Man.
✨ Build an AI with anA3C (Asynchronous Advantage Actor-Critic) model and train it to fight Kung Fu.
✨ Build an AI with aPPO (Proximal Policy Optimization) model and train it for a Self-Driving Car.
✨ Build an AI with aSAC (Soft Actor-Critic) model and train it for a Self-Driving Car.
✨ Build an AI byfine-tuning a powerful pre-trained LLM (Llama by Meta) with Hugging Face and re-train it to chat with you about medical terms. Simply put, we build here anAI Doctor Chatbot.
Some of these AIs will be built inAWS, and the others will be built inPython andPyTorch.
But that's not all... Once you complete the course, you will get3 extra AIs:DDPG,Full World Model, andEvolution 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 afree 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 until then, enjoy AI!
Who this course is for
⭐ Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning
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