Batchflow course

This repo contains study materials for course on machine and deep learning using batchflow framework.

1. Presentations folder contains pdfs with slides for presentations.
2. Tutorials folder contains interactive ml and dl demos (find even more here!).
3. Demo folder contains demos from real business cases.

Batchflow itself is embedded into repo as a git submodule.

Note, that all materials here are not self-sufficient and mean to act as auxiliary tool when teaching data science concepts.

See also recommended materials for self-study:

1. Introduction to statistical learning: main ML concepts with minimal mathematical explanations. Focused on classical models (linear models, kNN, trees, forests and so on). Great choice for a first book.
2. Deep learning: excellent first part includes math basics of ML. The rest of the book is a strong explanation of neural networks.
3. Глубокое обучение: погружение в мир нейронных сетей.

We also can advice you a great Stanford University course CS231n: Convolutional Neural Networks for Visual Recognition, containing both detailed lecture slides and repo with assignments.