All notes and materials for the CS229: Machine Learning course by Stanford University

Machine learning-Stanford University

my octave exercises for 2011 stanford machine learning class, posted after the due date of course

machine learning course programming exercise

Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.

Stanford Machine Learning course exercises implemented with scikit-learn

All notes and materials for the CS229: Machine Learning course by Stanford University

Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course (CS231n).

This Repository contains Solutions to the Quizes & Lab Assignments of the Machine Learning Specialization (2022) from Deeplearning.AI on Coursera taught by Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig.

Contains the Course Material and Assignment Solutions for the Machine Learning Course at Stanford University on Coursera.

A DOTA 2 hero recommendation engine for Stanford's CS 229 Machine Learning course.

Course Webpage for CS 217 Hardware Accelerators for Machine Learning, Stanford University

This repository contains all the programming exercises in Python for the Coursera course called "Machine Learning" by Adjunct Professor Andrew Ng at Stanford University.

Solutions to assignments of the CS224W Machine Learning with Graphs course from Stanford University.

Personal notes for course CS229 Machine Learning @ Stanford 2020 Spring

Machine Learning Notes (Mainly from Stanford CS229 and Coursera courses taught by Andrew Ng)

# Machine Learning (Coursera) This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. ## Contents * Lectures Slides * Solution to programming assignment * Solution to Quizzes by Andrew Ng, Stanford University, [Coursera](https://www.coursera.org/learn/machine-learning/home/welcome) ### Week 1 - [X] Videos: Introduction - [X] Quiz: Introduction - [X] Videos: Linear Regression with One Variable - [X] Quiz: Linear Regression with One Variable ### Week 2 - [X] Videos: Linear Regression with Multiple Variables - [X] Quiz: Linear Regression with Multiple Variables - [X] Videos: Octave/Matlab Tutorial - [X] Quiz: Octave/Matlab Tutorial - [X] Programming Assignment: Linear Regression ### Week 3 - [X] Videos: Logistic Regression - [X] Quiz: Logistic Regression - [X] Videos: Regularization - [X] Quiz: Regularization - [X] Programming Assignment: Logistic Regression ### Week 4 - [X] Videos: Neural Networks: Representation - [X] Quiz: Neural Networks: Representation - [X] Programming Assignment: Multi-class Classification and Neural Networks ### Week 5 - [X] Videos: Neural Networks: Learning - [X] Quiz: Neural Networks: Learning - [X] Programming Assignment: Neural Network Learning ### Week 6 - [X] Videos: Advice for Applying Machine Learning - [X] Quiz: Advice for Applying Machine Learning - [X] Videos: Programming Assignment: Regularized Linear Regression and Bias/Variance - [X] Machine Learning System Design - [X] Quiz: Machine Learning System Design ### Week 7 - [X] Videos: Support Vector Machines - [X] Quiz: Support Vector Machines - [X] Programming Assignment: Support Vector Machines ### Week 8 - [X] Videos: Unsupervised Learning - [X] Quiz: Unsupervised Learning - [X] Videos: Dimensionality Reduction - [X] Quiz: Principal Component Analysis - [X] Programming Assignment: K-Means Clustering and PCA ### Week 9 - [X] Videos: Anomaly Detection - [X] Quiz: Anomaly Detection - [X] Videos: Recommender Systems - [X] Quiz: Recommender Systems - [X] Programming Assignment: Anomaly Detection and Recommender Systems ### Week 10 - [X] Videos: Large Scale Machine Learning - [X] Quiz: Large Scale Machine Learning ### Week 11 - [X] Videos: Application Example: Photo OCR - [X] Quiz: Application: Photo OCR ## Certificate * [Verified Certificate]() ## References [[1] Machine Learning - Stanford University](https://www.coursera.org/learn/machine-learning)

Source code associated with final project for Machine Learning Course (CS 229) at Stanford University; Used reinforcement learning approach in a SUMO traffic simulation environment

My attempt at homework problems and programming assignments for Stanford's cs224w, Machine Learning with Graphs (2021) course.

My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs (GNN, GAT, GraphSAGE, GCN)

Here are the quiz answers and programming assignments' solutions for the course "Machine Learning" and five specializations in Coursera taught by Mr. Andrew Ng from Stanford University.

Topics - Linear Regression, Logistic Regression, Regularization, Neural Networks, System Design, Support Vector Machines, Unsupervised Learning (k-Means algorithm for clustering), Dimensionality Reduction (principal components analysis), Anomaly Detection, Recommender Systems, Large Scale Machine Learning, and Photo Optical Character Recognition.

A review of Linear Algebra in Persian (Based on the Machine Learning course, CS229, offered by Stanford)

A fully curated, open-source Data Science curriculum focused on Python. Includes top-tier university courses (MIT, Stanford, Princeton) covering essential topics in computer science, data analysis, machine learning, and statistics — everything you need to build a solid foundation in Data Science, 100% free.

Code used for research paper, "Using LSTM and SARIMA Models to Forecast Cluster CPU Usage" for the Stanford CS 229 Machine Learning Course

Programming assignments from Coursera's Machine Learning(by Stanford University) course taught by Andrew Ng.

Machine Learning Course by Stanford on Coursera (Andrew Ng)

CS229 course notes from Stanford University on machine learning, covering lectures, and fundamental concepts and algorithms. A comprehensive resource for students and anyone interested in machine learning.

Coursera Stanford Machine Learning course in Julia

💡This repository contains all of the lecture exercises of Machine Learning course by Andrew Ng, Stanford University @ Coursera. All are implemented by myself and in MATLAB/Octave.