The Hands-on Reinforcement Learning Course

Reinforcement Learning (RL) is the kind of machine learning closest to how humans and animals learn. It offers us a path towards building general AI systems that can tackle the most complex problems we can think of.

In this hands-on course you will start from the fundamentals of RL to advanced Deep RL.

The Hands-on Train & Deploy ML Tutorial

In this tutorial you won't build an ML system that will make you rich. But you will master the MLOps frameworks and tools you need to build ML systems that, together with tons of experimentation, can take you there.

With this hands-on tutorial, I want to help you grow as an ML engineer and go beyond notebooks.

Hands-on LLMs

In the Hands-on LLM tutorial you will learn how to build, step-by-step, a stock market advisor. You will do that by combining MLOps best practices, with the latest advancements in Large Language Models.

This course is brought to you by Paul Iusztin, Alexandru Răzvanț and myself.

Build and Deploy a Real-time Feature Pipeline with Python

Machine Learning models are as good as the input features you feed at training and inference time. And for many real-world applications, like financial trading, these features must be generated and served as fast as possible, so the ML system produces the best predictions possible. Generating and serving features fast is what a real-time feature pipeline does.

And in this course you will build one, from scratch, with Python.

Fetch, transform and visualize real-time data in Python

This repository shows how to

• fetch real-time trade data from the Coinbase Websocket API
• transform trade data into OHLC data in real-time using Bytewax, and
• plot the OHLC data using Bokeh, and
• deploy the whole thing with Streamlit Cloud.