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Machine Learning Katas
overview
Introduction to Machine Learning
Machine Learning in action
Introduction to Reinforcement Learning
Fundamentals
Handling data
Assessing results
Training models
Algorithms
Classic Machine Learning
K-Nearest Neighbors
Linear Regression
Logistic Regression
Decision Trees & Random Forests
Bayesian Methods
Support Vector Machines
K-Means
Neural Networks and Deep Learning
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Autoencoders
Neural Style Transfer
Generative Adversarial Networks
Transformers
Engineering
Introduction to MLOps
Machine Learning issues
Tools
Python
The Python ecosystem
Python cheatsheet
Python good practices
NumPy
Keras
PyTorch
Reference
Activation functions
Glossary
Acknowledgments
Binder
Colab
Repository
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Transformers
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