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