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
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Day 29 - Optimizers
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Day 30 - Loss Functions
Let me know if there is any correction. Feedback is welcomed.
Maya: Datetimes for Humansโข Datetimes are very frustrating to work with in Python, especially when dealing with different locales on different systems
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You can perform Land Cover Classification on Satellite Images using Random Forest and visualize the result using Earthpy package. Make sure to install the required packages and such as
This project has Classification and Clustering done Via kNN and K-Means respectfully. It later tests its efficiency via F1/accuracy/recall/precision for kNN and Davies-Bouldin Index for Clustering. T