ML-DL Python Notebooks

Explore 50 categorized Python notebooks for machine learning and deep learning. From regression to deep learning, this repository provides a guide for training, testing, evaluating, fine-tuning, and optimizing models. Enhance your skills with practical examples and comprehensive documentation.

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ML-DL Python Notebooks

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
Assessing the Performance of various Models.ipynb		Assessing the Performance of various Models.ipynb
Choosing features and metrics for nearest neighbor search.ipynb		Choosing features and metrics for nearest neighbor search.ipynb
Classification using Sentiment Analysis.ipynb		Classification using Sentiment Analysis.ipynb
Data Structure.ipynb		Data Structure.ipynb
Data Visualization.ipynb		Data Visualization.ipynb
Deep Learning Assignment.ipynb		Deep Learning Assignment.ipynb
Documet retrieval using clustering.ipynb		Documet retrieval using clustering.ipynb
Exploring Ensemble methods.ipynb		Exploring Ensemble methods.ipynb
Exploring precision and recall .ipynb		Exploring precision and recall .ipynb
Final_dataset.ipynb		Final_dataset.ipynb
Gradient Boosted tree.ipynb		Gradient Boosted tree.ipynb
Handling Overfitting in decision tree.ipynb		Handling Overfitting in decision tree.ipynb
House_prediction.ipynb		House_prediction.ipynb
Identifying safe loans with decision trees.ipynb		Identifying safe loans with decision trees.ipynb
Image Classification using Deep Learning.ipynb		Image Classification using Deep Learning.ipynb
Image Retrieval using Deep Features.ipynb		Image Retrieval using Deep Features.ipynb
Implementing LASSO using coordinate descent.ipynb		Implementing LASSO using coordinate descent.ipynb
Implementing Locality Sensitive Hashing from scratch.ipynb		Implementing Locality Sensitive Hashing from scratch.ipynb
Implementing binary decision trees.ipynb		Implementing binary decision trees.ipynb
Implementing logistic Regression from Scratch.ipynb		Implementing logistic Regression from Scratch.ipynb
K-Nearest Neighbour for predicting house prices.ipynb		K-Nearest Neighbour for predicting house prices.ipynb
Logistic Regression with L2 regularization.ipynb		Logistic Regression with L2 regularization.ipynb
Logistic Regression_Linear Classifier.ipynb		Logistic Regression_Linear Classifier.ipynb
Mercari Price Suggestion Lightgbm.ipynb		Mercari Price Suggestion Lightgbm.ipynb
Multiple Regression Assignment 1.ipynb		Multiple Regression Assignment 1.ipynb
Multiple Regression Assignment 2.ipynb		Multiple Regression Assignment 2.ipynb
Overfitting_Demo_Ridge_Lasso.ipynb		Overfitting_Demo_Ridge_Lasso.ipynb
Price optimization Part 1.ipynb		Price optimization Part 1.ipynb
Price optimization Part 2.ipynb		Price optimization Part 2.ipynb
Price optimization upgraded version.ipynb		Price optimization upgraded version.ipynb
Products_Recommender_1.ipynb		Products_Recommender_1.ipynb
Products_Recommender_2.ipynb		Products_Recommender_2.ipynb
README.md		README.md
Recommendation System.ipynb		Recommendation System.ipynb
Recommendation for Electronics products.ipynb		Recommendation for Electronics products.ipynb
Ridge Regression via Gradient desecent.ipynb		Ridge Regression via Gradient desecent.ipynb
Ridge regression using L2 penalty.ipynb		Ridge regression using L2 penalty.ipynb
Simple Regression Assignment.ipynb		Simple Regression Assignment.ipynb
Simple_Regression.ipynb		Simple_Regression.ipynb
Training Logistic Regression via Stochastic Gradient Ascent.ipynb		Training Logistic Regression via Stochastic Gradient Ascent.ipynb
Turi Getting Started with SFrames.ipynb		Turi Getting Started with SFrames.ipynb
Using Lasso to select Features.ipynb		Using Lasso to select Features.ipynb

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