A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook format ready to run in Google Colaboratory

You can find the credits for the authors in the header of each Jupyter Notebook.

• Machine Learning
• Deep Learning
• Reinforcement Learning

• Supervised Learning plots

• Unsupervised Learning plots

• Machine Learning Basic concepts 1 & 2 & 3 & 4

• Linear Regression 1 & 2 & 3

• Decision Trees 1

• Random Forest 1 & 2 & 3 & 4

• Naive Bayes 1 & 2

• k-Nearest Neighbor 1 & 2

• k-Means 1 & 2 & 3

• Support Vector Machines 1

• Logistic Regression 1 & 2 & 3

• Perceptron 1

• Machine Learning Overview 1

• Principal Component Analysis 1 & 2

• Topic modeling 1

• Ensembles 1 & 2

• GPU testing 1

• Artificial Neural Networks from scratch 1

• ANN Activation functions 1

• ANN Loss functions 1

• ANN Gradients 1 & 2

• ANN Optimizers 1

• ANN Decision Boundaries 1

• ANN Overfitting 1 & 2

• ANN Regularization 1

• Multi-Layer Perceptron 1 & 2 & 3 & 4 & 5

• Convolutional Neural Networks 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9

• CNN from scratch 1

• Data Augmentation 1 & 2 & 3

• Recurrent Neural Networks 1 & 2 & 3 & 4 & 5

• Autoencoders 1

• Generative Adversarial Networks 1 & 2 & 3 & 4 & 5

• AutoML 1 & 2 & 3

• Reinforcement Learning 1 & 2 & 3

Contributions welcome! Read the contribution guidelines first.