{"id":13513285,"url":"https://github.com/anishLearnsToCode/ml-stanford","last_synced_at":"2025-03-31T01:32:23.081Z","repository":{"id":98363139,"uuid":"272007701","full_name":"anishLearnsToCode/ml-stanford","owner":"anishLearnsToCode","description":"Machine Learning Course by Stanford on Coursera (Andrew Ng) ","archived":false,"fork":false,"pushed_at":"2022-02-20T22:04:26.000Z","size":29548,"stargazers_count":17,"open_issues_count":0,"forks_count":11,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-24T19:13:51.084Z","etag":null,"topics":["andrew-ng","classification","coursera","logistic-regression","machine-learning","ml","regression","stanford"],"latest_commit_sha":null,"homepage":"https://www.coursera.org/learn/machine-learning","language":"MATLAB","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/anishLearnsToCode.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}},"created_at":"2020-06-13T12:49:08.000Z","updated_at":"2025-02-26T15:19:36.000Z","dependencies_parsed_at":"2023-04-06T08:03:30.134Z","dependency_job_id":null,"html_url":"https://github.com/anishLearnsToCode/ml-stanford","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anishLearnsToCode%2Fml-stanford","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anishLearnsToCode%2Fml-stanford/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anishLearnsToCode%2Fml-stanford/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anishLearnsToCode%2Fml-stanford/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/anishLearnsToCode","download_url":"https://codeload.github.com/anishLearnsToCode/ml-stanford/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246403961,"owners_count":20771526,"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":["andrew-ng","classification","coursera","logistic-regression","machine-learning","ml","regression","stanford"],"created_at":"2024-08-01T04:00:49.612Z","updated_at":"2025-03-31T01:32:20.754Z","avatar_url":"https://github.com/anishLearnsToCode.png","language":"MATLAB","readme":"# Machine Learning ~ Stanford   \n\n![made-with-matlab](https://img.shields.io/badge/Made%20with-MATLAB-1f425f.svg)\n![problems-solved](https://img.shields.io/badge/Problems%20Solved-100%25-1abc9c.svg)\n[![course-list](https://img.shields.io/badge/also%20see-Other%20Coursera%20Courses-1f72ff.svg)](https://github.com/anishLearnsToCode/course-list)\n\n## 📖 Overview\n- [Week 1](#week-1)\n- [Week 2](#week-2)\n- [Week 3](#week-3)\n- [Week 4](#week-4)\n- [Week 5](#week-5)\n- [Week 6](#week-6)\n- [Week 7](#week-7)\n- [Week 8](#week-8)\n- [Week 9](#week-9)\n- [Week 10](#week-10)\n- [Week 11](#week-11)\n- [Certificate](#-certificate)\n\n## Week 1\n### Quizzes\n- [Introduction](week1/introduction.pdf)\n- [Linear Regression With One Variable](week1/linear-regression-one-variable.pdf)\n- [Linear Algebra](week1/linear-agebra.pdf)\n\n## Week 2\n### Quizzes\n- [Linear Regression With Multiple Variables](week2/linear-regression-multiple-variables.pdf)\n- [Octave/Matlab Tutorial](week2/octave-matlab-tutoria.pdf)\n\n### Programming Exercises\n- [Questions and Explanation](week2/ex1.pdf)\n- [Exercise 1](week2/ex1)\n    - [Warm Up Exercise](week2/ex1/warmUpExercise.m)\n    - [Compute Cost for One Variable](week2/ex1/computeCost.m)\n    - [Compute Cost For Multiple Variables](week2/ex1/computeCostMulti.m)\n    - [Gradient Descent For One Variable](week2/ex1/gradientDescent.m)\n    - [Gradient Descent For Multiple Variables](week2/ex1/gradientDescentMulti.m)\n\n## Week 3\n### Quizzes\n- [Logistic Regression](week3/logistic-regression-quiz.md)\n- [Regularization](week3/regularization-quiz.md)\n\n### Programming Exercises\n- [Questions](week3/ex2.pdf) \n- [Exercise 2](week3/ex2)\n    - [Sigmoid Function](week3/ex2/sigmoid.m)\n    - [Logistic Regression Cost](week3/ex2/costFunction.m)\n    - [Logistic Regression Gradient](week3/ex2/costFunction.m)\n    - [Regularized Logistic Regression Cost](week3/ex2/costFunctionReg.m)\n    - [Regularized Logistic Regression Gradient](week3/ex2/costFunctionReg.m)\n    - [Predict](week3/ex2/predict.m)\n\n## Week 4 \n### Quizzes\n- [Neural Networks: Representation](week4/neural-networks-quiz.md)\n\n### Programming Exercises\n- [Questions and Explanations](week4/machine-learning-ex3/ex3.pdf)\n- [Exercise 3](week4/machine-learning-ex3/ex3)\n    - [Logistic Regression Cost Function](week4/machine-learning-ex3/ex3/lrCostFunction.m)\n    - [One vs. All Multi Class Classifier](week4/machine-learning-ex3/ex3/oneVsAll.m)\n    - [Predict one vs. all Multi Class Classifier](week4/machine-learning-ex3/ex3/predictOneVsAll.m)\n    - [Neural Network Prediction Function](week4/machine-learning-ex3/ex3/predict.m)\n\n\n## Week 5\n### Quizzes\n- [Neural Networks: Learning](week5/neural-networks-quiz.md)\n\n### Programming Exercises\n- [Questions and Explanations](week5/ex4.pdf)\n- [Exercise 4](week5/ex4)\n    - [Feedforward and Cost Function](week5/ex4/nnCostFunction.m)\n    - [Regularized Cost Function](week5/ex4/nnCostFunction.m)\n    - [Compute Gradient of Sigmoid Function](week5/ex4/sigmoidGradient.m)\n    - [Randomly Initialize Weights](week5/ex4/randInitializeWeights.m)\n    - [Neural Network Gradient (Backpropagation)](week5/ex4/checkNNGradients.m) \n    - [Neural Network Cost Function](week5/ex4/nnCostFunction.m)\n    - [Regularized Gradient](week5/ex4/checkNNGradients.m)\n\n## Week 6\n### Quizzes\n- [Advice for Applied Machine Learning](week6/advice-for-applying-machine-learning.md)\n- [Machine  Learning System Design Quiz](week6/machine-learning-system-design-quiz.md)\n\n### Programming Exercises\n- [Questions and Explanations](week6/ex5.pdf)\n- [Exercise 5](week6/ex5)\n    - [Regularized Linear Regression Cost Function](week6/ex5/linearRegCostFunction.m)\n    - [Generate Learning Curve](week6/ex5/learningCurve.m)\n    - [Maps Data into Polynomial Feature Space](week6/ex5/polyFeatures.m)\n    - [Generates a Cross Validation Curve](week6/ex5/validationCurve.m)\n\n## Week 7\n### Quizzes\n- [Support Vector Machines (SVM)](week7/support-vector-machines-quiz.md)\n\n### Programming Exercises\n- [Questions and Explanations](week7/ex6.pdf)\n- [Exercise 6: Support Vector Machines](week7/ex6)\n    - [Gaussian Kernel for SVM](week7/ex6/gaussianKernel.m)\n    - [Parameters to Use for Dataset 3](week7/ex6/dataset3Params.m)\n    - [Email Preprocessing](week7/ex6/processEmail.m)\n    - [Feature Extraction From Email](week7/ex6/emailFeatures.m)\n\n## Week 8\n### Quizzes\n- [Unsupervised Learning](week8/unsupervised-learning-quiz.md)\n- [Principal Component Analysis](week8/principal-component-analysis.md)\n\n### Programming Exercises\n- [Exercise 7: Questions and Explanations](week8/ex7.pdf)\n- [K Means Clustering and PCA(Principal Component Analysis)](week8/ex7)\n    - [Perform PCA(Principal Component Analysis)](week8/ex7/pca.m)\n    - [Project a Dataset into lower dimensional space](week8/ex7/projectData.m)\n    - [Recover the Original Data from the Projection](week8/ex7/recoverData.m)\n    - [Find Closest Centroids Using K-Means](week8/ex7/findClosestCentroids.m)\n    - [Compute Centroid Means](week8/ex7/computeCentroids.m)\n    - [Initialize K means for Centroids](week8/ex7/kMeansInitCentroids.m)\n\n## Week 9\n### Quizzes\n- [Anomaly Detection](week9/anomaly-detection-quiz.md)\n- [Reccomender Systems](week9/reccomender-systems-quiz.md)\n\n### Programming Exercises\n- [Exercise 8: Questions and Explanations](week9/ex8.pdf)\n- [Anomaly Detection and Reccomender Systems](week9/ex8)\n    - [Estimate Gaussian Parameters](week9/ex8/estimateGaussian.m)\n    - [Find Threshold for Anomaly Detection](week9/ex8/selectThreshold.m)\n    - [Cost Function for Collaborative Filterinf](week9/ex8/cofiCostFunc.m)\n\n## Week 10\n### Quizzes\n- [Large Scale Machine Learning](week10/large-scale-ml-quiz.md)\n\n## Week 11\n### Quizzes\n- [Application: Photo OCR](week11/application-photo-ocr-quiz.md)\n\n## 🎓 [Certificate](http://coursera.org/verify/PY3HEUJFNZ2M)\n![certificate](assets/certificate.PNG)\n","funding_links":[],"categories":["Future Scope and Path"],"sub_categories":["Machine Learning 🔥"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FanishLearnsToCode%2Fml-stanford","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FanishLearnsToCode%2Fml-stanford","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FanishLearnsToCode%2Fml-stanford/lists"}