Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
15 Hour(s) 15 Minute(s)
Language
English
Taught by
Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, Luka Anicin, Ligency
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Combine the power of Agentic AI, Generative AI, Reinforcement Learning to create powerful AI for Real-World applications
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 8 different AIs:
Build an AI Agent with a Foundation Model (LLM) for business assistance, all powered by the Cloud.
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 – 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 Content
- 22 section(s)
- 128 lecture(s)
- Section 1 Welcome to the course!
- Section 2 Agentic AI
- Section 3 ---------- Part 0 - Fundamentals Of Reinforcement Learning ----------
- Section 4 Q-Learning Intuition
- Section 5 Q-Learning Implementation
- Section 6 ---------- Part 1 - Deep Q-Learning ----------
- Section 7 Deep Q-Learning Intuition
- Section 8 Deep Q-Learning Implementation
- Section 9 ---------- Part 2 - Deep Convolutional Q-Learning ----------
- Section 10 Deep Convolutional Q-Learning Intuition
- Section 11 Deep Convolutional Q-Learning Implementation
- Section 12 ---------- Part 3 - A3C ----------
- Section 13 A3C Intuition
- Section 14 A3C Implementation
- Section 15 ---------- Part 4 - PPO and SAC ----------
- Section 16 ---------- Part 5 - Intro to Large Language Models (LLMs) ----------
- Section 17 LLMs Intuition
- Section 18 LLMs Implementation
- Section 19 THANK YOU
- Section 20 Annex 1: Artificial Neural Networks
- Section 21 Annex 2: Convolutional Neural Networks
- Section 22 Congratulations!! Don't forget your Prize :)
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
Skills covered in this course
Reviews
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JJakub Majewski
The material was presented in an accessible way. The practical tasks with step-by-step instructions were very helpful.
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KKonrad Leszczyński
The code is often impossible to read, eg. this single line does 5 things: next_states, rewards, dones, infos, _ = map(np.array, zip(*[env.step(a) for env, a in zip(self.envs, actions)])) The author should read eg. "Clean code" by Bob Martin
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RRoger Jackson
This was a useful introduction to both AI and LLMs. Whilst not a pre-req some previous programming\coding knowledge would be beneficial.
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EEkaterina Sertakova
It wasn’t easy — the concepts were complex and sometimes mind-bending — but that’s exactly what made it so rewarding. The course offered a deep dive into the technologies that are shaping the future, and it left me inspired to explore new ways of applying AI in practice.
