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Machine Learning

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.

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Rating 4.9 based on 22,037 ratings
Length 12 weeks
Starts Oct 12 (3 weeks ago)
Cost $79
From Stanford University via Coursera
Instructor Andrew Ng
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Analysis & Statistics Data Science Machine Learning

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What people are saying

logistic regression

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.

The course offers an overview on linear regression, logistic regression, neural network, support vector machine, regularization, validation, and some unsupervised machine learning methods.

I've tried other approaches before, like diving head first into neural networks without a clue about other simpler algorithms like linear and logistic regression and just got confused despite having no trouble with the mathematics.

I am currently up to week 3 on Logistic Regression and loving every minute of it.

Topics covered: linear regression, logistic regression, neural networks, SVMs, K-Means clustering, anomaly detection, etc.

My only suggestion is that during the concluding video provide a better summary of how all of the different approaches fit together ( Deep learning, nueral networks, linear regression, logistic regression, etc.

Thank you Just Excellent, everything about this course is just fantastic, beginning with Prof.Andrew, passing with his passion for the subject and his motivation to really make you understand everyword he says, he is keen on delivering all this expertise and this alone is a fine quality, the course is well organized and the quizzes and programing assignements are to the point and are a very good exercise, I just felt the course needed 2 small videos one addresssing the differences between linear regression, logistic regression, SVMs and Neural Networks and another video exciting people by a small example of machine learning on self-driving cars (very small programming assignments to help excite people and give them an-overall idea)again Thanks to Prof.Andrew and all who helped me find my hobby and passion :D the best course for machine learning beginners excellent course Informative, concise, and practical.

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anomaly detection

I strongly believe this course is a must have if you're starting in neural network, but also great for any kind of machine learning problem like image recognition, data classifier and anomaly detection, function modeling and so on.

Neural Networks or Anomaly Detection).

Especially in the Anomaly Detection, the usage of PDF was very ambiguous.

I also learned what I've learned when I was in the university like anomaly detection and neural network.

Topics like supervised/unsupervised learning, Anomaly Detection, Computer Vision though harsh are intuitively introduced to learners and clearly explained.

This course is perfect,I enrolled in April, and finished in August.In the beginning, I don't think this course will help me a lot in machine learning, but now I think what I thought in the beginning is absolutely wrong, I learned very much in this course, including logistic regression, linear regression, regularization, bias and variance, anomaly detection, principal component analysis, clustering algorithm-keans, recommender system, multiclassification, support vector machine, kernel methods, gaussian distribution, stochastic gradient descent, map-reduce, artificial neural network and backpropagation octave programming etc.If you want to break into machine learning, I think this course will help you to do so.

fraud detection) via anomaly detection algorithms.

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support vector machines

Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).

He takes you through behind-the-scenes of magic of regression, classification, clustering, neural networks, support vector machines in a very lucid manner.

Enjoyable, easy-to follow and yet quite detailed introduction to things like logistic regression, neural networks, support vector machines, or recommender systems.

Turns out machine learning is just math, and this course teaches the calculus & linear algebra concepts needed to understand linear regression, neural networks, support vector machines, K-means clustering, and more.

In that regard, I found the lectures on support vector machines sadly very confusing (I learned more by downloading Andrew Ng's lectures notes from his actual Stanford course).It's nevertheless a good introductory course and I would recommend it to anybody who wants to learn the basics of machine learning.

For example, for Support Vector Machines and backpropagation for neural networks.

Andrew was a very good teacher, taking time to explain fundamentals of linear regression, logistic regression, neural networks, support vector machines, case studies and more.

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getting bogged down

This course does a great job of surveying some of the most popular and useful machine learning algorithms and digs into the math enough to give good intuition without getting bogged down in the details.

I really enjoyed Andrew Ng's teaching style, and even though I have a fairly extensive background in math, I appreciated his emphasis on applying these techniques without getting bogged down in proofs, etc.

The difficulty often lies finding that boundary — the boundary where the complexity of a computation or a problem or a strategy can be abstracted out (with a black-box, or an analogy) and a student can make progress in thinking about the problem without getting bogged down.

Great teaching style , Presentation is lucid, Assignments are at right difficulty level for the beginners to get an under the hood understanding without getting bogged down by the superfluous details.

Lots of tips and tricks are given to help troubleshoot problems that often occur in practice.The programming exercises are designed so that the student can focus on understanding the essential topics instead of getting bogged down in too many details (nevertheless, it's a good idea to briefly go through the functions and files already written by the staff).

The tests are very well tuned to learning the key points while not getting bogged down in technical difficulties or data issues.

The explanations are thorough without being boring or getting bogged down in minute details.

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inner workings

For me, Machine learning had mostly been magic... until I enrolled in Andrew's course which dispelled the magic and explained the inner workings to someone who is new to this field as clearly as possible.

It's an amazing addition to the curricula especially after several years after my graduation.For some people might be too basic, for some others like me, it was precisely what I needed to know, to understand the inner workings, to be able to "tune up the car, and not just be a driver".

This course is excellent for understanding the inner workings of Machine learning.

Go deep into the fundamentals rather than just using scripts that you don't understand the inner workings of.

Andrew Ng found the perfect balance between explaining seemingly complicated concepts in simple terms, while at the same time still giving enough mathematical detail that one gets to understand the "inner workings" of the machine learning techniques presented in this course.

Really descriptive and thorough, teaching you the fundamentals and inner workings behind machine learning and more advanced topics as well.

The course doesn't just treat the algorithms as black boxes, but rather explains the mathematics and inner workings of them.

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black box

There are many courses out there that will teach you to plug and chug on a black box, but Professor Ng unwraps that mystery with enough math to provide some intuition on how things are working.

I finally got to understand things like Neural Networks in depth, which to me was a black box until this course.

No black boxes here.

This course would not suit anyone looking to do machine learning using an entirely black box approach, with the algorithms entirely hidden.

Methods like logistic regression, SVM and Neural Networks become easy to comprehend and are not a "black box" anymore.

Very interesting, easy to follow videos (right level of detail on linear algebra) and lots of algorithms, techniques and tips!For the last lessons, however, the practice is a bit "black box" where you have everthing initialized and you just plug in your functions, I'd liked more details on how everything was set.

It's just enough math so that the algorithms aren't strictly treated like black boxes.

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An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Research Scientist-Machine Learning $55k

Cloud Architect - Azure / Machine Learning $75k

Watson Machine Learning Engineer $81k

Machine Learning Software Developer $103k

Software Engineer (Machine Learning) $116k

Applied Scientist, Machine Learning $130k

Autonomy and Machine Learning Solutions Architect $131k

Applied Scientist - Machine Learning -... $136k


Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k


Sorted by most helpful reviews first

Guest says:

Amazing. I learned regression (linear and logistic), k-means clustering, and principal components analysis in uni, but seeing it in an ML context reinforced the grasp I have over those topics. Neural networks were entirely new to me and by far one of the most interesting concepts I've learned from anywhere!

Guest says:

this is a very good introduction to machine learning, i have already used it on two projects about for image recognition and customer service logs from company server. if you google it, you can also find many machine learning libraries for python, C++, etc. but in my opinion you can use those more effectively if you know what is happening behind the scene.

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Rating 4.9 based on 22,037 ratings
Length 12 weeks
Starts Oct 12 (3 weeks ago)
Cost $79
From Stanford University via Coursera
Instructor Andrew Ng
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Analysis & Statistics Data Science Machine Learning

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