30 Days Of Machine Learning Using Pytorch

Overview

MLWithPyTorch

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

๐Ÿ“Œ Day 1 - Linear Regression
๐Ÿ“Œ Day 2 - Logistic Regression
๐Ÿ“Œ Day 3 - Decision Tree
๐Ÿ“Œ Day 4 - KMeans Clustering
๐Ÿ“Œ Day 5 - Naive Bayes
๐Ÿ“Œ Day 6 - K Nearest Neighbour (KNN)
๐Ÿ“Œ Day 7 - Support Vector Machine
๐Ÿ“Œ Day 8 - Tf-Idf Model
๐Ÿ“Œ Day 9 - Principal Components Analysis
๐Ÿ“Œ Day 10 - Lasso and Ridge Regression
๐Ÿ“Œ Day 11 - Gaussian Mixture Model
๐Ÿ“Œ Day 12 - Linear Discriminant Analysis
๐Ÿ“Œ Day 13 - Adaboost Algorithm
๐Ÿ“Œ Day 14 - DBScan Clustering
๐Ÿ“Œ Day 15 - Multi-Class LDA
๐Ÿ“Œ Day 16 - Bayesian Regression
๐Ÿ“Œ Day 17 - K-Medoids
๐Ÿ“Œ Day 18 - TSNE
๐Ÿ“Œ Day 19 - ElasticNet Regression
๐Ÿ“Œ Day 20 - Spectral Clustering
๐Ÿ“Œ Day 21 - Latent Dirichlet
๐Ÿ“Œ Day 22 - Affinity Propagation
๐Ÿ“Œ Day 23 - Gradient Descent Algorithm
๐Ÿ“Œ Day 24 - Regularization Techniques
๐Ÿ“Œ Day 25 - RANSAC Algorithm
๐Ÿ“Œ Day 26 - Normalizations
๐Ÿ“Œ Day 27 - Multi-Layer Perceptron
๐Ÿ“Œ Day 28 - Activations
๐Ÿ“Œ Day 29 - Optimizers
๐Ÿ“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)
Owner
Mayur
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