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Back to Supervised Machine Learning: Regression and Classification

Learner Reviews & Feedback for Supervised Machine Learning: Regression and Classification by DeepLearning.AI

4.9
stars
24,349 ratings

About the Course

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....

Top reviews

FA

May 24, 2023

The course was extremely beginner friendly and easy to follow, loved the curriculum, learned a lot about various ML algorithms like linear, and logistic regression, and was a great overall experience.

AD

Nov 23, 2022

Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.

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4501 - 4525 of 4,797 Reviews for Supervised Machine Learning: Regression and Classification

By Kimia E

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Sep 6, 2022

If the course was more programming-oriented it would be better. All in all the course was really helpful and very easy to understand.

By Thi M T D

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Aug 18, 2023

I highly recommended this course. The only downside aspect here was some unclear suggestions in the code of the last lab assignment.

By Ravesh B

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Apr 21, 2023

A decent intro into some of the core ML techniques that are out there and how to use them. Really enjoyed the pace of the lecturer.

By William M

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Oct 3, 2023

There should be some way to download formatted notes of key points from the lectures, and to download the submitted quiz content.

By jieyou w

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Jun 18, 2023

Very Clear and concise teaching of concepts. Good Effort! Thanks to Andrew and the team behind this course. will sign up for more

By Abhi s

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Dec 24, 2022

It's a great course I loved the videos

maths behind the algorithms are explend flowlessly

I wish it focused on practice bit more

By Ariel G

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Nov 5, 2022

Deberían agregar subtítulos en español para abarcar más personas.

They should add subtitles in Spanish to cover more people.

By Koushik V M

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Aug 23, 2022

The lab classes could also be thought as a compulsory part of the course ,otherwise a really good and a recommendable course.

By Sai G M

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Jul 21, 2022

It was fantastic! Andrew is a very good instructor. He made most of the concepts crystal clear while explaining the ideas.

By Sree C

•

May 18, 2024

make every lab graded instead of optional. so that we can practice every lab seriously and helps for better understanding

By Hasnat A A

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Feb 8, 2023

it would be much better if the labs were also explained a bit because there were a lot of things that were quite unseen.

By Elead B

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Oct 28, 2024

Great course. I would recommend for someone that has a fair mathematical knowledge. I struggled a bit with the formula

By KESHAV

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Jun 12, 2023

I would like it more if problems like K-means clustering and SVMs were also discussed in the lectures and/or labs.

By Mairi M

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May 3, 2024

Excellent teaching and high quality learning materials. The jump between theory and practical was a little steep

By Yousef R

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Jul 30, 2022

ts a very helpful course to get into AI, i would sa it could use a bit more coding in the videos to demonistrate

By Moutassi B G

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Sep 1, 2024

The basics are perfectly and so simply explained and all is done such you must understand what you are studying

By Shiva T

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Feb 20, 2023

Some more practical examples can be included but the course material and topics and ecplaination were great.

By Amit S

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Dec 18, 2022

Every concept was explained in a very easy and interesting way. Really liked the course and way of teaching.

By Anukul D

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Nov 10, 2022

I actually got the right course at right time and thank you to coursera for providing the course. Hats Off!!

By Raman K P

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Dec 18, 2022

Real life dataset use would have been more helpful.

Also, use of scikit-learn could have been explored more.

By Kunal G

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Aug 16, 2022

Good One, the course is to the point . Please include linear algebra as it was added in the older version .

By Royston L

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Jun 21, 2022

I don't understand why the practice lab code for gradient descent and the lab assignment code is different.

By Fang H

•

Jan 23, 2024

Explained the complex concepts in very clear and simple way. Labs are very helpful and very well designed.

By Samuel S

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Jul 16, 2022

It get's exponetially harder as the weeks go by. This course could really use more programming excercises!

By Alzahra A A

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Jul 21, 2023

A great course, very informative and easy to understand.

Wish there were more project based assignments.