About This Course
Combine the power of Data Science, Machine Learning and Deep 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 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 AIÂ 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!
Build 7 different AIs for 7 different applications
Understand the theory behind Artificial Intelligence
Master the State of the Art AI models
Bathiya P.
In my experience the course is good and understandable but it is certainly not for All Levels. If you have not done at least a basic course in Machine Learning at the least then this course will be beyond your understanding level. In addition, it would have been nice to refer back to the theory explanations while the implementation was being done instead of the long verbal explanations of the instructor while typing the code in Colab notebooks. This is especially relevant to A3C implementation where due to not implementing the implementation according to what is explained in the theory sections will look daunting to understand at the beginning. E.g. A3C implementation in the implementation "does not" use a multiple agent setup although the required PyTorch multiprocessing packages are imported at the top and also there is no incorporation of LSTM in the A3C implementation although in theory section the instructors specifically mention we will be implementing A3C LSTM according to paper by Google. In addition in the theory section the instructor just skims through Google's paper on A3C pseudocode and specifically says the implementation instructor will explain the pseudocode while implementing which is of course not the case.. :) Apart from these few issues, the course is delivered in a well planned manner and if these few issues can be fixed, it would become a Great course worth 5 stars. But do not attempt this course if you don't have any prior experience in at least a basic ML course. Do that first and then come here which will give you the most benefit for what you are paying.