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This book is still under construction. We appreciate your patience as we get it completed. Feedback is welcome!
Introduction
Regression
1.
Linear Regression
2.
Assessing Performance
3.
Ridge Regularization
4.
Feature Selection and LASSO Regularization
Classification
5.
Classification Overview
6.
Logistic Regression
7.
Bias and Fairness
8.
Naïve Bayes
9.
Decision Trees
10.
Ensemble Methods
Deep Learning
11.
Neural Networks (coming soon)
12.
Convolutional Neural Networks (coming soon)
Document Retrieval / Local Methods
13.
Introduction, Precision/Recall, k-Nearest Neighbors (coming soon)
14.
Kernel Methods (coming soon)
15.
Locality Sensitive Hashing (coming soon)
16.
Clustering & k-means (coming soon)
17.
Hierarchical Clustering (coming soon)
Recommender Systems
18.
Dimensionality Reduction (coming soon)
19.
Recommender Systems
Repository
Open issue
Index
