Machine Learning from scratch

Implementation of machine learning algorithms from scratch

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The following algorithms are implemented to date:

1. Train Test Split
2. Stratified Split
3. K-Fold Cross Validation
4. Stratified K-Fold Cross Validation
5. Linear Regression with Batch Gradient Descent optimizer
6. Linear Regression with Mini-Batch Gradient Descent optimizer
7. Least Absolute Deviation (LAD) Regression
8. Multiple Linear Regression
9. Logistic Regression
10. Multiple Logistic Regression
11. K Nearest Neighbors
12. K-Means Clustering
13. Implementing Receiver Operating Characteristic (ROC) Curve
14. Implementing Precision Recall Curve (PRC)
15. Word2Vec implementation in Keras
16. Word2Vec implementation in PyTorch
17. Simulating Random Walks
18. How random are Python numbers?
19. Principal Component Analysis
20. Singular Value Decomposition
21. Collaborative Filtering
22. Bag of Words (BOW) modelling
23. Linear Discriminant Analysis (LDA) v/s Principal Component Analysis (PCA)
24. Understanding Loss Functions
25. Multi-Label Classification
26. Recommendation Evaluation Metrics
27. Hierarchical Clustering
28. Density-based spatial clustering of applications with noise (DBSCAN)
29. Naive Bayes Classifier