Blog posts, course materials, forums, books etc. for studying maching learning and statistics.

Continuously updated.

Statistics

• An Accidental Statistician, George E. P. Box
• Introduction to Statistics with R, Andrew Conway, DataCamp (online course), 1, 2, 3, 4
• Statistical Modeling, Daniel Kaplan, DataCamp (online course)
• Naive Bayes classifier, Wikipedia
• Oversampling and Undersampling in Data Analysis, Wikipedia
• Confusion Matrix, Wikipedia
• When Can You Safely Ignore Multicollinearity?, Statistical Horizons

Learning

• How a Kalman filter works, in pictures, Bzarg
• Learning Reinforcement Learning (with Code Exercises and Solutions)

Machine Learning

• Machine Learning, Andrew Ng, Coursera
• UFLDL Tutorial, Stanford
• The Master Algorithm, Pedro Domingos
• Introduction to Machine Learning, Vincent Vankrunkelsven, DataCamp
• Machine Learning Mastery

Machine Learning - Advanced

• CS224n: Natural Language Processing with Deep Learning, Stanford
• Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs, wildml
• Understanding LSTM Networks, colah’s blog
• A Primer on Neural Network Models for Natural Language Processing, arXiv, Yoav Goldberg
• Natural Language Understanding with Distributed Representation, Lecture Note, arXiv, Kyunghyun Cho

R

• R Courses, David Wickham, DataCamp
• Machine Learning ToolBox, Zachary Deane-Mayer, Max Kuhn, DataCamp
• How To Use R For Machine Learning, Machine Learning Mastery

Tensorflow

• Tensorflow and deep learning – without a PhD, Martin Görner, Youtube
• https://github.com/hunkim
• https://github.com/TensorFlowKR/awesome_tensorflow_implementations