Implementation of some machine learning algorithms

The following algorithms/use-cases are implemented to date:

1. Gaussian Mixture Models
2. Who should host the party? - Geometric Median
3. Evaluating K-Means Clustering
4. Multi-Layer Perceptron (MLP) in Keras
5. Multi-Layer Perceptron (MLP) in PyTorch
6. t-Distributed Stochastic Neighbor Embedding (t-SNE)
7. File handling (.JSON/.TXT/.PNG/.H5/.PARQUET/.NPY/.PKL)
8. Web Scraping using Beautiful Soup in Python
9. Saving/loading/converting deep learning models - PyTorch, Keras, ONNX, TFLite
10. Serving Sentiment Analysis Model using Flask API
11. Understanding statistical concepts - CDF, Sampling, Law of Large Numbers
12. Plotting Decision Boundaries
13. Monte Carlo Simulations
14. Understanding Non-Maximal Suppression (NMS) in Image Processing
15. Understanding Kalman Filters
16. Mixed Precision Training using TPU (Google Colab)
17. Understanding Self-attention in NLP
18. Building Knowledge Graph on MovieLens 1M (ML1M) Dataset with Networkx
19. Implementing PageRank algorithm on famous social networks
20. Building recommendation system on ML1M with PageRank
21. Implementing RecWalk on ML1M
22. Understanding various optimizers - GD, SGD, Momentum, AdaGrad, RMSProp, Adam
23. GLoVe implementation from scratch: Paper
24. Sparse Latent Semantic Analysis (LSA): Paper
25. Latent Dirichlet Allocation (LDA) - Estimating topic distributions with Gibbs Sampling (Part 1/2) Video
26. Latent Dirichlet Allocation (LDA) and its applications - Probabilistic generative model (Part 2/2) Video
27. Word Sense Disambiguation - Knowledge based approaches - Lesk, Walker, Random Walk (Part 1/2) Video
28. Word Sense Disambiguation - ML based approaches - Decision List, Yarowsky's, HyperLex (Part 2/2) Video
29. Projected Gradient Descent - Gradient Descent with constraints
30. Support Vector Machines (SVM) from scratch
31. Optimization beyond Gradient Descent - Linear SVM, Logistic Regression, Non-Negative OLS Regression
32. Neural Networks - Building MLP from scratch
33. AutoGluon - AutoML
34. RNN from scratch Video
35. Matrix Factorization based Collaborative Filtering using Gradient Descent