Popular and less popular machine learning and data processing algorithms implemented in Python Reference: "Machine Learning: A Bayesian Optimization Perspective" by Sergios Theodoridis https://www.elsevier.com/books/machine-learning/theodoridis/978-0-12-801522-3
Majority of the algorithms are implemented by myself from scratch based on the theory from the reference, unless otherwise noted in the scripts. For each algorithm there will be a notebook test document and a clean python script.
The algorithms implemented in this repository include:
1. Adaboost
2. Adaptive Projected Subgradient Method (APSM)
3. Convolutional Neural Network (CNN)
4. Compressed Sensing Matching Pursuit (CSMP)
5. Decision tree
6. Fuzzy C Means
7. Hierarchical and DBSCAN Clustering
8. Iterative Shrinkage/Thresholding (IST) algorithms
9. Kernal PCA
10. K-means family
11. KNN
12. Linear Discriminant Analysis (LDA)
13. Linear Regression
14. Logistic Regression
15. Multipule layer ANN
16. Naive Bayes
17. Orthogonal Matching Pursuit (OMP)
18. Principal Component Analysis (PCA)
19. Support Vector Machine (SVM)
20. The Primal Estimated Subgradient Solver for SVM (PEGASOS)
21. Perceptron
22. Sequential feature selection (SBS)
Some applications of the algorithms, such as sentiment analysis using ANN, are not listed in here. But please explore.