Contains all course modules, exercises and notes of ML Specialization by Andrew Ng, Stanford Un. and DeepLearning.ai in Coursera

Machine Learning by Stanford University. Programming assignments.

Exercises submitted to the Machine Learning course by Stanford done through Coursera

Coursera/Stanford Machine Learning course assignments in python

Final Project for the CS22W: Machine Learning with Graphs course at Stanford University (http://web.stanford.edu/class/cs224w/) in Autumn '21. We applied graph ML techniques and networks (such as the Graph Convolutional Network, GraphSAGE Network and Graph Isomorphism Network) to the ogbl-ddi dataset (https://ogb.stanford.edu/docs/linkprop/#ogbl-ddi) for drug-drug interactions.

All the execrcises from the stanford machine learning course

Course description: "Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas."

Solutions to Stanford ML Course problem sets (CS229)

Stanford ML course by Andrew Ng

Labs completed as part of the online ML course offered by Stanford

Contains all course modules, exercises and notes of ML Specialization by Andrew Ng, Stanford Un. and DeepLearning.ai in Coursera

I've implemented the simplest of the Machine Learning algorithms in C-Language. It's a follow along repository with ML Stanford University (Andrew NG's) course.

Repository of Andrew Ng's Coursera Course on Machine Learning. Heavily borrowed from mstampfer/Coursera-Stanford-ML-Python

Coursera/Stanford Machine Learning course assignments in python

Exercise solutions to Stanford ML course

Programming Assignment

Personal handwritten notes for Stanford’s ML Course by Prof. Andrew Ng

Coursera - Machine Learning by Andrew Ng

Coursera Stanford Machine Learning assignments in R langauge and Python

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Stanford ML Course on Coursera

Machine Learning Course (Andrew Ng, Stanford) from Coursera

Andrew Ng ML

Solutions to the online ML course by Andrew Ng

Andrew Ng's ML intro code logs

Stanford University ML course - Coursera

Repository to deposit the files and code Andrew's Ng ML Course

andrew ng's coursera course. stanford university machine learning

This repo contains solution to all the programming assignment of Machine Learning(Coursera) taught by Andrew Ng

This contains the assignments of the Andrew NG course on Machine Learning