{"id":20698240,"url":"https://github.com/urbanclimatefr/machine-learning-coursera-python","last_synced_at":"2026-04-24T07:35:03.984Z","repository":{"id":261244295,"uuid":"345358636","full_name":"urbanclimatefr/machine-learning-coursera-python","owner":"urbanclimatefr","description":"This repository contains python Implementation of certain programming assignments of Andrew Ng’s Machine Learning Course on Coursera, created by Stanford University.","archived":false,"fork":false,"pushed_at":"2021-03-07T13:57:18.000Z","size":25003,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-19T15:56:41.833Z","etag":null,"topics":["coursera","machine-learning","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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created by Stanford University.\n\n* Programming Exercise 1: Linear Regression\\\nIn this exercise, you will implement linear regression and get to see how it work on real world datasets.\n\n* Programming Exercise 2: Logistic Regression\\\nIn this exercise, you will implement logistic regression and apply it to two different datasets.\n\n* Programming Exercise 3: Multi-class Classification and Neural Networks\\\nIn this exercise, you will implement one-vs-all logistic regression and feedforward propagation for neural networks to recognize handwritten digits.\n\n* Programming Exercise 4: Neural Network Learning\\\nIn this exercise, you will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.\n\n* Programming Exercise 5: Regularized Linear Regression and Bias vs Variance\\\nIn this exercise, you will implement regularized linear regression and polynomial regression and use it to study models with different bias-variance properties.\n\n* Programming Exercise 6: Support Vector Machines\\\nIn this exercise, you will implement support vector machine (SVM) with Gaussian Kernels and you will be using support vector machines (SVMs) to build a spam classifier.\n\n* Programming Exercise 7: K-means Clustering and Principal Component Analysis\\\nIn this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images.\n\n* Programming Exercise 8: Anomaly Detection and Recommender Systems\\\nIn this exercise, you will implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, you will use collaborative filtering to build a recommender system for movies.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Furbanclimatefr%2Fmachine-learning-coursera-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Furbanclimatefr%2Fmachine-learning-coursera-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Furbanclimatefr%2Fmachine-learning-coursera-python/lists"}