30 Days Of Machine Learning Using Pytorch

30 Days Of Machine Learning Using Pytorch

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)