## 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**

ðŸ“Œ Day 1 - Linear Regression

ðŸ“Œ Day 2 - Logistic Regression

ðŸ“Œ Day 3 - Decision Tree

ðŸ“Œ Day 4 - KMeans Clustering

ðŸ“Œ Day 5 - Naive Bayes

ðŸ“Œ Day 6 - K Nearest Neighbour (KNN)

ðŸ“Œ Day 7 - Support Vector Machine

ðŸ“Œ Day 8 - Tf-Idf Model

ðŸ“Œ Day 9 - Principal Components Analysis

ðŸ“Œ Day 10 - Lasso and Ridge Regression

ðŸ“Œ Day 11 - Gaussian Mixture Model

ðŸ“Œ Day 12 - Linear Discriminant Analysis

ðŸ“Œ Day 13 - Adaboost Algorithm

ðŸ“Œ Day 14 - DBScan Clustering

ðŸ“Œ Day 15 - Multi-Class LDA

ðŸ“Œ Day 16 - Bayesian Regression

ðŸ“Œ Day 17 - K-Medoids

ðŸ“Œ Day 18 - TSNE

ðŸ“Œ Day 19 - ElasticNet Regression

ðŸ“Œ Day 20 - Spectral Clustering

ðŸ“Œ Day 21 - Latent Dirichlet

ðŸ“Œ Day 22 - Affinity Propagation

ðŸ“Œ Day 23 - Gradient Descent Algorithm

ðŸ“Œ Day 24 - Regularization Techniques

ðŸ“Œ Day 25 - RANSAC Algorithm

ðŸ“Œ Day 26 - Normalizations

ðŸ“Œ Day 27 - Multi-Layer Perceptron

ðŸ“Œ Day 28 - Activations

ðŸ“Œ Day 29 - Optimizers

ðŸ“Œ Day 30 - Loss Functions

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

## References

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