TF
5.0Reviewed Jul 17, 2023
I hope more and more engineers in Japan take this course.The joy of learning machine learning with the world's top lecturer far outweighs the pain learning the subject in the non-native language.
Unsupervised Learning, Recommenders, Reinforcement Learning
In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. 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.
Status: Machine Learning
Status: Data Ethics
BeginnerCourse28 hours
Featured reviews
AS
5.0Reviewed Oct 9, 2024
This is a good beginner course . I have been reading around topics and when you jumble around it is hard to follow. This course structure was what i was looking for. i would recommend this to others
SA
5.0Reviewed Sep 22, 2025
Thank you so much this is a great course, and thanks for the financial aid that enabled me to study the course and improve my skills and career. this specialization is so valuable and useful.
SB
5.0Reviewed Nov 6, 2022
This course is a brief but thorough introduction. It has a good mixture of theory and practice.Andrew Ng explains every thing very good, understandable and in a fun way.I highly recommend this class!
KA
5.0Reviewed Oct 12, 2024
This is an excellent introduction to supervised learning and reinforcement learning. It's an excellent course for any beginner in this field. Very easy to follow and very much enjoyable!
HI
4.0Reviewed Feb 28, 2024
Very well on introduction the basic concept of the topics, but some of the function are not visible enough for us to understand (such as the update boolean in the assessment of reinforcement learning)
MK
5.0Reviewed Apr 30, 2023
So much time to spend, so much math to understand, but it's really fun to gain knowledge from this course especially machine learning intuition for me who had passion on that. Thankyou!
AS
5.0Reviewed Jun 1, 2025
this was a very good course for build a very strong foundation of machine learnignn and many advance this were also taught, with a whole lot of guidence on every step. really appricated thsi course .
HZ
5.0Reviewed Jul 21, 2024
Got insights to what Recommender systems are and how it recommends user based on their usage and got to know how Reinforcement learning works and Successfully landed the lunar lander.
YN
4.0Reviewed May 1, 2026
By enrolling this course, you will be able to learn theoretical knowledge about ML algorithms very well. But if we want to master ML, we need to practice more than what's provided in this course.
GR
4.0Reviewed Aug 13, 2025
The course has been very informational, and even though it had some complex topics, it never felt too complicated to understand as all the explanations and examples were really great.
AV
4.0Reviewed Feb 29, 2024
Amazing content, perfectly curated topics with hands-on labs, although Assignments and labs could be more challenging based on certain level students who already have programming backgrounds.