An interactive and visual Machine Learning book - with code and assignment

Learn AI together, for free

Prerequisites 👨‍💻

Python is one of the most popular, flexible programming languages today. You can use it for everything from basic scripting to Machine Learning.

Start to read

Data Science 💾

Data Science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract insights from data.

Start to read

Machine Learning Basics 🚀

Machine Learning (ML) is a field devoted to understanding and building methods that let machines “learn” – that is, methods that leverage data to improve computer performance on some set of tasks.

Start to read

Advanced Machine Learning 🛸

Goes beyond the foundational concepts and explores advanced methods to tackle complex problems, including topics like clustring models, ensemble learning, Gradient Boosting and so on.

Start to read

Deep Learning 🤖

Deep Learning is part of a broader family of Machine Learning methods, which is based on artificial neural networks with representation learning in supervised, or unsupervised way.

Start to read

MLOps ⚒️

Machine Learning Operations, also known as MLOps, is focused on streamlining the process of taking models to production, and then maintaining and monitoring them.

Start to read

Features

Empower the learning journey with interactive Jupyter Notebooks.

Tutorial 👩‍🏫

The book is built with Jupyter Book from computational content written as notebook.

Assignment 📚

The assignment is in TDD style and self-testable as pytest compatible notebook.

Slide 📺

Built with RISE which instantly turns notebook into a live reveal.js presentation.

Transforming Machine Learning through the art of visualization.

Python 🐍

Python is visualized by Python Tutor, a free tool for visualizing code execution.

Data Science 💾

Data Science is visualized by Pandas Tutor, Pandas and other visualization libraries.

Machine Learning 🚀

Algorithms and models are visualized by popular frameworks like Tensorflow.js.

Connect with us

We are an international open-source community that welcomes discussion, feedback, and contributions of many kinds. Here are a few ways to connect more with us.

💬 Ask and answer questions

We have community discussions, talk about ideas, and share general questions and feedback in our community forum.

👍 Vote for new content or features

Provides feedback by adding a 👍 reaction to issues in our repositories. You can find the top issues in our GitHub project.

🙌 Contribute to projects

We welcome anyone to join us in improving this book and helping one another learn AI. To join, check out our contributing guide.

🌍 About our team

This book is developed by the Ocademy community. Check out our GitHub homepage to learn more about us and how we work.

Acknowledgements

This book is supported by an open community of contributors, many of whom come from the Shanghai University.