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These are the notes for CS229, a machine learning course taught by Andrew Ng at Stanford University. The notes cover topics such as supervised learning, kernel methods, support vector machines, deep learning, and generalization.
Star 169. master. README. MIT license. Machine Learning By Prof. Andrew Ng 🌟🌟🌟🌟⭐. This page contains all my YouTube/Coursera Machine Learning courses and resources 📖 by Prof. Andrew Ng 👨. Table of Contents. Brief Intro. Video lectures Index. Programming Exercise Tutorials. Programming Exercise Test Cases. Useful Resources. Schedule.
Machine-Learning-Notes. Collection of my hand-written notes, lectures pdfs, and tips for applying ML in problem solving. Resource are mostly from online course platforms like DataCamp, Coursera and Udacity.
A complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng. The notes cover topics from regression, neural networks, support vector machines, clustering, dimensionality reduction, anomaly detection, recommender systems and more.
Complete and detailed pdf plus handwritten notes of Machine Learning Specialization 2022 by Andrew Ng in collaboration between DeepLearning.AI and Stanford Online in Coursera, Made by Arjunan K.
CS229 Lecture Notes. Andrew Ng. (updates by Tengyu Ma) Supervised learning. Let's start by talking about a few examples of supervised learning problems. Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: Living area (feet2) Price (1000$s) 2104. 400 1600. 330 2400. 369 1416. 232 3000 ... 540 ...
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learning problem, it will be up toyoutodecidewhatfeaturesto choose,soifyouareoutinPortland gatheringhousingdata,youmight also decide to include other fea-turessuchaswhethereachhouse hasafireplace,thenumberofbath-rooms,andsoon.We’llsaymore aboutfeatureselectionlater,butfor nowlet’stakethefeaturesasgiven.
