Machine learning theory made easy.

So, you want to master machine learning. Even though you have experience in the field, sometimes you still feel that something is missing. A look behind the curtain.

Have you ever felt the learning curve to be so sharp that it was too difficult even to start? The theory was so dry and seemingly irrelevant that you were unable to go beyond the basics?

If so, I am building something for you. I am working to create the best resource out there to study the fundamentals of machine learning.

Join the early access and be a part of the journey!

Math explained, as simple as possible. Every concept is explained step by step, from elementary to advanced. No fancy tricks and mathematical magic. Intuition and motivation first, technical explanations second.

Open up the black boxes. Machine learning is full of mysterious black boxes. Looking inside them allows you to be a master of your field and never be in the dark when things go wrong.

Be a part of the process. This book is being written in public. With early access, you’ll get each chapter as I finish, with a personal hotline to me. Is something not appropriately explained? Is a concept not motivated with applications? Let me know, and I’ll get right on it!

Book preview!

If you would like to see a sample, I got you! Check it out: https://mlfz.readthedocs.io/

What you'll get?

• The latest version of the books, in an interactive Jupyter Book format + pdf.

• Exclusive access to a new sub-chapter as I finish them. (See my planned roadmap below.)

• A personal hotline to me where you can share your feedback with me to build the best learning resource for you.

Refund policy

If you find that the Early Access Program is not for you, no worries! Let me know within 30 days of your purchase, and I'll refund you immediately - no questions asked.

Preliminary table of contents

Part 1. Machine learning

1. What is machine learning?
2. Linear and logistic regression
3. The makings of a machine learning framework
4. Foundations of supervised learning
5. Decision trees
6. Foundations of unsupervised learning
7. Machine learning in practice

Part 2. Neural networks

8. Computational graphs
9. The backward pass
10. Vectorized computational graphs
11. Advanced optimization
12. Convolutional networks