Objective of the repository is to learn and build machine learning models using Pytorch.

List of Algorithms Covered

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

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