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

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Homework of Andrew Ng's "Machine Learning" course in Coursera

This repository contains all the Lab and Assignments from Andrew NG Machine Learning Specialization Course on Coursera.

This contains notes and exercises made in Python I made a long time ago from the Andrew Ng course in Coursera.

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Coursera-Machine-Learning-Fall2013-4thEd-AndrewNg

ترجمه زیرنویس دوره یادگیری ماشین Andrew Ng دانشگاه استنفورد

吴恩达机器学习课程笔记 。

Programming assignments for Coursera's Machine Learning Course.

All the solutions from the programming assignments of the Machine Learning Course in Coursera taught by Andrew NG, Professor of Stanford University

吴恩达老师的《机器学习》课程作业，python实现。

Apuntes del curso de machine learning de la universidad de Stanford disponible en coursera (Andrew Ng)

吴恩达Coursera课程《机器学习》课后作业代码&笔记

吴恩达机器学习系列课程的编程作业，用python实现。

吴恩达机器学习编程作业原版和答案

Programing exercise of<Machine Learning> - Andrew Ng / 《机器学习》(吴恩达) 中的编程练习

The solutions to the exercises done during Machine Learning course by Andrew Ng on Coursera.

Getting hands dirty on some basic machine learning project while pursuing Machine learning on Coursera by AndrewNg

Contains my LaTeX notes for the course and the exercises.

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.

Programming assignments (with Octave and Python) from Coursera's Machine Learning course taught by Andrew Ng.

Machine Learning from Andrew Ng at coursera.org with Standford

[COMPLETED] - It contains the solved exercises for the basic machine learning course all solved by me. If you're getting help from my code. Please star the repo.

# [Deep Learning Specialization on Coursera](https://www.coursera.org/specializations/deep-learning) **Master Deep Learning, and Break into AI** Instructor: [Andrew Ng](http://www.andrewng.org/) This repo contains all my work for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, [Deep Learning Specialization on Coursera](https://www.coursera.org/specializations/deep-learning). ## Goals - Learn the foundations of Deep Learning - Understand how to build neural networks - Learn how to lead successful machine learning projects - Learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. - Work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. - Practice all these ideas in Python and in TensorFlow.

This contain all the solutions of Andrew NG Machine Learning course.

homework of machine learning

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iPython notebooks for Andrew Ng's Machine Learning course on Coursera

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