{"id":20698240,"url":"https://github.com/urbanclimatefr/machine-learning-coursera-python","last_synced_at":"2025-12-16T06:16:29.981Z","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":"2025-01-17T18:57:01.518Z","etag":null,"topics":["coursera","machine-learning","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/urbanclimatefr.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-03-07T13:50:01.000Z","updated_at":"2021-03-07T13:57:20.000Z","dependencies_parsed_at":"2024-11-05T14:38:41.274Z","dependency_job_id":null,"html_url":"https://github.com/urbanclimatefr/machine-learning-coursera-python","commit_stats":null,"previous_names":["urbanclimatefr/machine-learning-coursera-python"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/urbanclimatefr%2Fmachine-learning-coursera-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/urbanclimatefr%2Fmachine-learning-coursera-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/urbanclimatefr%2Fmachine-learning-coursera-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/urbanclimatefr%2Fmachine-learning-coursera-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/urbanclimatefr","download_url":"https://codeload.github.com/urbanclimatefr/machine-learning-coursera-python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242967662,"owners_count":20214280,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["coursera","machine-learning","python"],"created_at":"2024-11-17T00:23:49.457Z","updated_at":"2025-12-16T06:16:27.926Z","avatar_url":"https://github.com/urbanclimatefr.png","language":"Jupyter Notebook","readme":"# machine-learning-coursera-python\nThis repository contains python Implementation of certain programming assignments of Andrew Ng’s Machine Learning Course on Coursera, 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","funding_links":[],"categories":[],"sub_categories":[],"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"}