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

4.9
stars
23,866 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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51 - 75 of 4,717 Reviews for Supervised Machine Learning: Regression and Classification

By Manish M

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Sep 17, 2023

Programming assignment not giving proper explanation for failure

By Anusha V C

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Jul 8, 2024

The course is beginner friendly and provides an excellent starting point for anyone interested in machine learning. The content is well-structured, and the concepts are explained clearly with practical examples covering the essentials of regression and classification techniques. (The optional labs enhance the course by making coding more engaging and simpler). Professor Andrew Ng's teaching style is exceptional. He has a knack for breaking down complex topics into easily digestible lessons, making the learning process enjoyable and effective. I am also incredibly grateful for the financial aid, which made it possible for me to enroll in this course. Overall, I highly recommend this course to anyone looking to start their journey in machine learning. The combination of Professor Andrew Ng's excellent teaching, comprehensive content, and beginner-friendly approach makes it a standout learning experience.

By Michelle W

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Jun 20, 2022

Excellent course, it really lays the groundwork for understanding the concepts and some of the math behind it, and provides an opportunity to play with the python code in labs. This is a step up from "AI for Everybody", and a good prep for the Deep Learning Specialization. I'm a data analyst with some coding experience, prior coursework in calculus & linear algebra & basic statistics, and found this a great supplement as I'm also working through the Deep Learning Specialization.

By J R

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Jun 21, 2022

Fantastic introduction to Machine Learning. The labs have been updated with widgets. You can add data points, change the polynomial order and many other changes that makes this a great way to understand how the different components of machine learning are done. Highly recommend.

By Alireza S

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Jun 19, 2022

This is a great Machine Learning course for the first-time learners offered by the best in the field. IMHO, the focus of course is on learning the underlying theories of machine learning rather than short-circuiting the basic concepts to the helpers libraries developed in Python.

By Dingrui W

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Jul 26, 2022

Brilliant course! I really enjoy the journey and cannot wait to start the second course. It's such a great thing to have a course like this which is made with great endeavor. And spending time and thoughts on it is even more amazing. I am so lucky to encounter this course!

By Amelia H B

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Nov 6, 2023

It helped me clarify many confusions I had, I am no longer left with doubts, I can now make my own models, and I am very grateful. Professor Andrew is very clear with the concepts, and I don't even know mathematics, but I know what I have to do :) thanks!!!

By Pritam D

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Jun 30, 2022

Perfect balance of application and theory, and wise choices in ramping up the complexity gradually. Discussion boards are very helpful, feels very much like personalized learning. Thank you!

By DEVANSH B

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May 31, 2024

Great intro to supervised learning (regression & classification). Clear explanation of sigmoid function and decision thresholds. Could benefit from examples & exploring non-linear boundaries.

By Dan C

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Jun 23, 2022

Excellent course, very logical and well structured. Highly recommended to anyone interested in learning about this topic. Assignments are on the easy side but you learn a lot nonetheless.

By Vishnu V

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Jul 24, 2022

This was a great course to understand all the math and logic that goes behind some of the most commonly used ML algorithms. Interesting and a great start to the specialization.

By Reshendren N

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Nov 7, 2023

The course was brilliant and it presented very important ideas in a simple and easy to follow way. The depth and implications of the knowledge presented is quite profound.

By Ryan M

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Jun 25, 2022

Good for beginners. If you have taken the previous online course 'Machine Learning' taught by Prof. Andrew Ng, you may find this course much easier.

By Mohammed A B

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Jul 24, 2022

One of the best ML courses so far. The Course is well designed and very well presented by Andrew NG. I highly recommend it.

By Abhishek P

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Jun 20, 2022

Precise explanation of the fundamentals of Machine learning techniques, using mathematical examples and python.

By 马镓浚

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Aug 7, 2022

Very friendly for beginners, a good refresher if you already had the knowledge of machine learning.

By Alexander S

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Jun 17, 2022

- Amazing instructor

- Very clear and easy to understand examples

By Sayak M

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Mar 20, 2023

Great Great Great Course. Thank you for this amazing course

By Mohammad A V

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Oct 17, 2023

With all these jupyter-notebook labs its fantastic!

By Kahouli M

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Jul 24, 2022

ilove how simple and rich this course is

By Peter G

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Apr 19, 2024

very understandable

By Yu L

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Jul 29, 2022

Very clear and intuitive explanation with a great instructor, though the contents are a little too easy, especially for people with a STEM background. More exercise could be set with less guidance (currently it's like writing ten lines of codes for every week of learning). Also, it would be nice if there could be an exercise dedicated to the use of packages like scikit-learn in depth, since that is what most people will end up using the most.

By Kostas M

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Jul 5, 2022

A very good introduction to Machine Learning. I would prefer some more math since this gives me more confidence in understanding, but the course is aimed to a wide audience so that's acceptable. I accompanied the course with Andrew Ng's notes on machine learning.

By Gariman S

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Jul 11, 2022

Andrew sir's teaching made the course interesting and exciting. However, the course was too easy and some more mathematically oriented discussions could have been done.

By Preyas H

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Nov 7, 2023

A good intro to ML. Strikes a good balance of the theoretical and practical aspects of Supervised ML.