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

4.9
stars
8,144 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

JM

Sep 21, 2022

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

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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26 - 50 of 1,843 Reviews for Supervised Machine Learning: Regression and Classification

By Alejandro D S g

Sep 2, 2022

The course is good but once you cancel the subscription, you lose access to the codes. I think that should be change.

By Hamilton E

Aug 11, 2022

Too much theory and very few practice.

By Mikhail B

Nov 14, 2022

I have completed this course in full and as a result, I am highly satisfued with how Professor Andrew Ng explains the materials. Thank you for this! However, I cannot understand, why after completing the course a part of studying materials are not accessible, even though I paid a sufficient price for the course. These unaccessible materials include Python programs which were used as a practice. Frankly, I find it unfair, since this practice would be extremely important to revise the materials while improving my skills in Machine Learning in the future. Moreover, a part of the montly fee was paid also for the practice materials. I may agree that these Python programs can be private, however,there should be ways to overcome this issue. Without the possibility to revise the code it will be much harder to create our own applications and programs.

By Darshan H

Aug 5, 2022

Unable to Open the labs and submit the lab assignments

By Michelle W

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 JR

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

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

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 Juan J B M

Sep 22, 2022

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

By Ami D

Nov 24, 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.

By Pritam D

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 Dan C

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

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 Ryan M

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

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

Jun 20, 2022

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

By 马镓浚

Aug 7, 2022

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

By Alexander S

Jun 17, 2022

- Amazing instructor

- Very clear and easy to understand examples

By Sayak M

Mar 20, 2023

Great Great Great Course. Thank you for this amazing course

By Kahouli M

Jul 24, 2022

ilove how simple and rich this course is

By Yu L

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

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

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 Mubeen u h

Aug 2, 2022

very good course

By Rok Š

Nov 6, 2022

The focus of the whole course is on gradient descent. I guess it is needed for some other algorithms but here we could have just found the derivative. If I had no background in math and statistics I would give up ML seeing this.