30 Days Of Machine Learning Using Pytorch
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML 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)
