The objective of the repository is to learn and build machine learning models using Pytorch.
List of Algorithms Covered
๐ Day 1 - Linear Regression
๐ Day 2 - Logistic Regression
๐ Day 3 - Decision Tree
๐ Day 4 - KMeans Clustering
๐ Day 5 - Naive Bayes
๐ Day 6 - K Nearest Neighbour (KNN)
๐ Day 7 - Support Vector Machine
๐ Day 8 - Tf-Idf Model
๐ Day 9 - Principal Components Analysis
๐ Day 10 - Lasso and Ridge Regression
๐ Day 11 - Gaussian Mixture Model
๐ Day 12 - Linear Discriminant Analysis
๐ Day 13 - Adaboost Algorithm
๐ Day 14 - DBScan Clustering
๐ Day 15 - Multi-Class LDA
๐ Day 16 - Bayesian Regression
๐ Day 17 - K-Medoids
๐ Day 18 - TSNE
๐ Day 19 - ElasticNet Regression
๐ Day 20 - Spectral Clustering
๐ Day 21 - Latent Dirichlet
๐ Day 22 - Affinity Propagation
๐ Day 23 - Gradient Descent Algorithm
๐ Day 24 - Regularization Techniques
๐ Day 25 - RANSAC Algorithm
๐ Day 26 - Normalizations
๐ Day 27 - Multi-Layer Perceptron
๐ Day 28 - Activations
๐ Day 29 - Optimizers
๐ Day 30 - Loss Functions
- Sklearn Library
- ML-Glossary
- ML From Scratch (Github)