Machine Learning Using Pytorch

The objective of the repository is to learn and build machine learning models using Pytorch.

List of Algorithms Covered

📌 Day 1 - Linear Regression
📌 Day 2 - Logistic Regression
📌 Day 3 - Decision Tree
📌 Day 4 - KMeans Clustering
📌 Day 5 - Naive Bayes
📌 Day 6 - K Nearest Neighbour (KNN)
📌 Day 7 - Support Vector Machine
📌 Day 8 - Tf-Idf Model
📌 Day 9 - Principal Components Analysis
📌 Day 10 - Lasso and Ridge Regression
📌 Day 11 - Gaussian Mixture Model
📌 Day 12 - Linear Discriminant Analysis
📌 Day 13 - Adaboost Algorithm
📌 Day 14 - DBScan Clustering
📌 Day 15 - Multi-Class LDA
📌 Day 16 - Bayesian Regression
📌 Day 17 - K-Medoids
📌 Day 18 - TSNE
📌 Day 19 - ElasticNet Regression
📌 Day 20 - Spectral Clustering
📌 Day 21 - Latent Dirichlet
📌 Day 22 - Affinity Propagation
📌 Day 23 - Gradient Descent Algorithm
📌 Day 24 - Regularization Techniques
📌 Day 25 - RANSAC Algorithm
📌 Day 26 - Normalizations
📌 Day 27 - Multi-Layer Perceptron
📌 Day 28 - Activations
📌 Day 29 - Optimizers
📌 Day 30 - Loss Functions

Let me know if there is any correction. Feedback is welcomed.

References

• Sklearn Library
• ML-Glossary
• ML From Scratch (Github)