Machine Learning
Stanford University via Coursera

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Overview
Class Central Tips
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/nonparametric 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, antispam), computer vision, medical informatics, audio, database mining, and other areas.
Syllabus
 Introduction
 Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and uptodate information.
 Linear Regression with One Variable
 Linear regression predicts a realvalued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.
 Linear Algebra Review
 This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.
 Linear Regression with Multiple Variables
What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression. Octave/Matlab Tutorial
 This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.
 Logistic Regression
 Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multiclass classification.
 Regularization
 Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.
 Neural Networks: Representation
 Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
 Neural Networks: Learning
 In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition.
 Advice for Applying Machine Learning
 Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.
 Machine Learning System Design
 To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
 Support Vector Machines
 Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.
 Unsupervised Learning
 We use unsupervised learning to build models that help us understand our data better. We discuss the kMeans algorithm for clustering that enable us to learn groupings of unlabeled data points.
 Dimensionality Reduction
 In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.
 Anomaly Detection
 Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
 Recommender Systems
 When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and lowrank matrix factorization.
 Large Scale Machine Learning
 Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.
 Application Example: Photo OCR
 Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.
Taught by
Andrew Ng
Charts
 #1 in Subjects / Computer Science / Machine Learning
 #1 in Subjects / Machine Learning / Supervised Learning
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Reviews
4.7 rating, based on 377 reviews

Gregory J Hamel ( Life Is Study) completed this course and found the course difficulty to be medium.
Machine Learning is one of the first programming MOOCs Coursera put online by Coursera founder and Stanford Professor Andrew Ng. Although Machine learning has run several times since its first offering and it doesn’t seem to have been changed or updated... 
Anonymous completed this course.
Background elements: I'm an engineer by trade and have been working on statiscal projects in field of transport regulation for about ten years. I have some general background in maths and theorical computer science, I'm capable of programming. I followed... 
Anonymous completed this course.
This is possibly the most outstanding university class you will ever take. It is definitely the best university level course I have ever taken, and I have taken quite a few, both in person and online (MOOC). If you have any interest whatsoever in how... 
I hoped this course would be more hardcore and indepth, but I still found it useful. Video/audio quality is lastcentury, but explanations are quite nice and clear.

Anonymous completed this course.
I was able to finish this 11week MOOC in ten days because the materials are a fine balance between succinct and comprehensive and very engagingly presented. I was initially turned off by the use of MATLAB/Octave as the programming language of choice... 
Anonymous completed this course.
My opinion is very personal. In my view, taking a class rather then reading a book has one fundamental aim: make it easier and faster to get workable knowledge on a topic and to capitalize on it. In other word the objective of such a class should be:... 
1040
Mark Wilbur completed this course.
This course is famous. It’s taught by the equally famous Coursera cofounder and MLstar, Andrew Ng. Though I found this class to be one of the worst learning experiences I’ve had with a MOOC, I really have to say I love Andrew’s ability to explain things... 
1040
Scott Orr completed this course.
Andrew Ng is a clear and charismatic lecturer, he covers advanced techniques, and he provides a number of practical tips, but the programming exercises are a bit canned, and may not fully prepare students to write their own scripts in Octave. The exercises... 
Mal Minhas completed this course, spending 4 hours a week on it and found the course difficulty to be hard.
I published my thoughts on the course and its contents on in a blog post which you can find here: http://malm.teqy.net/machinelearningcoursera/ 
Szabolcs Sulik completed this course, spending 70 hours a week on it and found the course difficulty to be hard.
A clear, to the point course about a very broad material. I liked the breath and depth processing of the various topics. 
60
WickWack completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
Professor Ng is extremely clear. His lectures are extraordinarily wellorganized, thoughtful, and clear. The assignments are interesting, relevant, and not too difficult.
After completing the course, I took MIT's open Linear Algebra course, and at that point was able to get more of the mathematical background. Professor Ng was very careful to present the material without much math  impressive to say the least. However, once I got more of the mathematical background, I felt much more solid in my understanding. 
Ronny De Winter completed this course and found the course difficulty to be medium.
Andrew Ng can be considered as the godfather of machine learning education. As one of the founders of Coursera he created one of the most popular MOOCs on the internet.
If you want a good introduction to the Machine Learning knowledge field then this is a course for you. 
11
Paolo Perrotta is taking this course right now, spending 8 hours a week on it and found the course difficulty to be medium.
Low production values; terrible audio quality; a very traditional, mostly noninteractive approach... and yet, this course manages to be one of the best I've ever taken. The quality of Andrew Ng's teaching is just *that* good. He's a rare case of a worldlevel expert that's also extremely good at communicating his knowledge. This guy makes you wish you could shake his hand and buy him a beer at the end of each lesson.
This course proves that a skilled human with a whiteboard can still beat the bells and whistles of more expensively produced trainings. If you know little or nothing about Machine Learning, it will give you a solid foundation. 
1This Machine Learning course offered a comprehensive, technical introduction to different topics in Machine Learning. Being many years out of school, the mathematical components also take alot of time to refresh and digest. It would be good for you to revisit your linear algebra notes or videos on YouTube, such as the set of 18.06 Linear Algebra lectures by Gilbert Strang on YouTube or OCW.
The course exercises were in MATLAB and not in Python. If you wish to learn the equivalent implementation of the Machine Learning techniques in Python, you would need to search somewhere else outside of this course. 
5I wanted to dig into ML for a long time and it started with this course for it's "historical importance". I've studied some little pieces of it at university (like linear regression, least sq. method, etc) as a math student. The following review is very...

1040
John Johnson completed this course.
A lot of participants were concerned that it was a watered down version of Stanford’s CS229. And, in fact, the course was more limited in scope and more applied than the official Stanford class. However, I found this to be a strength. Because I was already familiar with most of the methods in the beginning (linear and multiple regression, logistic regression), I could focus more on the machine learning perspective that the class brought to these methods. Programming exercises were done in Octave, an open source Matlablike programming environment. 
Anonymous completed this course.
This course gave a thorough introduction to machine learning. It describes and explains many different models of supervised learning (e.g. linear regression, logistic regression, SVMs, neural networks) as well as unsupervised learning (e.g. Kmeans clustering).... 
Prose Simian completed this course, spending 7 hours a week on it and found the course difficulty to be medium.
Prof Ng simplifies ML as much as possible  and no more. In the complex arena of ML, that still leaves things fairly complex... But thanks to this course (which I'm 90% of the way through) I feel like I'll have a sufficient intuitive grasp of ML for vaguely... 
2Top recruiting companies for our course are Tech Mahindra, NFL, IFFCO, etc. The highest rate of placements in our course is 75%, and the highest salary package offered is 10 LPA. From our course, about 79% of the students do internships in different companies. Top roles offered in our course are a field officer and scientists.

6
Nitin Gupta completed this course.
I was completely new to ML but never felt lost while taking this course (completed yesterday). The programming assignments are a bit watered down in that most of the "boilerplate" is already written but you still get great insight with whatever is left for you to implement  in particular, learning to write vectorized code is what I found immensely useful.