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Stanford University

Supervised Machine Learning: Regression and Classification

Stanford University via Coursera


In the first course of the Machine Learning Specialization, you will:
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.


  • Week 1: Introduction to Machine Learning
    • Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!
  • Week 2: Regression with multiple input variables
    • This week, you'll extend linear regression to handle multiple input features. You'll also learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code.
  • Week 3: Classification
    • This week, you'll learn the other type of supervised learning, classification. You'll learn how to predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization at the end of this week!

Taught by

Andrew Ng


4.7 rating, based on 381 Class Central reviews

4.9 rating at Coursera based on 2494 ratings

Start your review of Supervised Machine Learning: Regression and Classification

  • 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

    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...
  • I​ hoped this course would be more hardcore and in-depth, but I still found it useful. Video/audio quality is last-century, but explanations are quite nice and clear.
  • Anonymous

    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...
  • Anonymous

    Anonymous completed this course.

    I was able to finish this 11-week 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

    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:...
  • Mark Wilbur

    Mark Wilbur completed this course.

    This course is famous. It’s taught by the equally famous Coursera co-founder and ML-star, 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...
  • Anonymous
    It was a great journey, I got excited in many times and got frustrated in many other times Pros of the course: 1. A thorough course that you can rely on to form a foundation in the field. 2. A well-designed course, that's not purely theoretical nor...
  • Scott Orr

    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...
  • Anonymous

    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. K-means clustering)....
  • 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:
  • Mubarik Shafi Abaoli
    I am a student from Ethiopia and want to learn new technology. I think it will beneficial for my career. But I have no job of my own to carry the expenses of to pay for the certificate of this course. I live only for my scholarship it is very difficult...
  • Kai
    This 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.
  • WickWack

    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 well-organized, 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.
  • Paolo Perrotta

    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 non-interactive 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 world-level 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.
  • 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.
  • John Johnson

    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 Matlab-like programming environment.
  • 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...
  • Profile image for Komal Gupta
    Komal Gupta
    Top 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.

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