In this series, we cover statistical experimentation, modeling, and machine learning, focusing on:
basic statistics, hypothesis testing, and experimentation
validation methodology
linear and logistic regression; generalized linear models
causality
time series analysis
Bayesian statistics
clustering
decision trees, random forests, and boosted forests
artificial neural networks and deep learning
reinforcement learning
Our focus is on mathematical derivations, algorithmic development, and programming. We use Python throughout the text to support our theory. All machine learning techniques are coded from scratch using Python.
basic statistics, hypothesis testing, and experimentation
validation methodology
linear and logistic regression; generalized linear models
causality
time series analysis
Bayesian statistics
clustering
decision trees, random forests, and boosted forests
artificial neural networks and deep learning
reinforcement learning
Our focus is on mathematical derivations, algorithmic development, and programming. We use Python throughout the text to support our theory. All machine learning techniques are coded from scratch using Python.
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