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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

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.

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

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1376 - 1400 of 4,794 Reviews for Supervised Machine Learning: Regression and Classification

By Muhammad A H

Feb 11, 2023

Good course for beginners, who looking to learn machine learning.

Everything was deeply explained by my dear instructor.

By Nishshanka C D S

Dec 25, 2022

I am a beginner in ML and AI. I learned so much valuable things during this course. Thank you very much for the effort.

By Abhay K S

Dec 9, 2022

Exceptional introduction for anyone looking to get their hands wet in the world of ML. No prior coding skills required!

By Yhobany M G

Sep 26, 2022

Andrew Ng es simplemente espectacular para enseñar los conceptos basicos pero determimantes para el Machine Learning.

By Navead J

Sep 1, 2022

Excellent course. I just wish they used more linear algebra and less algebra, but you can figure that out on your own.

By Peter H S

Aug 29, 2022

Outstanding course with excellent teaching. Interactive Jupyter notebooks explain critical concepts of supervised ML.

By Samuel Z

Jul 29, 2022

Very gentle introduction to ML concepts.

Definitely a great starting point for beginners looking to get their toes wet.

By Leon L

Mar 3, 2024

Very interesting course to understand the basic principles of supervised learning with linear and logistic regression.

By Aditya S

Feb 2, 2024

Andrew Ng is the best person to learn ML from. The concepts are so well built and this has given me a great foundation

By Abdoul A M

Jan 5, 2024

I learned so much, it's incredible ! the explanation are so clear, I understood but I need more time to really get it.

By Sohag H (

Aug 21, 2023

Thank you Andrew NG sir for giving the opportunity .I will bring your knowledge into rural area . I have learned a lot

By OneX

Jul 2, 2023

i don't know whether this is the best machine learning or not, but the tutor is top notch and very easy to understand.

By Leon “ J

Feb 28, 2023

A little easy for a student with some knowledge in this area. But as for a starting course, it's alright and detailed.

By Aditya B

Feb 4, 2023

This course gave me confidence. I enjoyed learning all the amazing stuff about machine learning

Thanks a lot Dr. Andrew

By Rafi D

Jan 30, 2023

Clarity of explanations and testing only what matters makes this course fairly painless and yet extremely informative.

By Apurva R

Jan 26, 2023

Excellent course. One addition to the existing material I would request is a course on Bayesian Probability, MLE, MAP.

By Chih-Ta W

Nov 26, 2022

This course is well-designed and the labs really helped a lot in understanding the concepts mentioned in the lectures.

By Pranil D

Oct 30, 2022

Really great intro to ML. It didn't feel too complicated and that's largely due to the great instructor Andrew Ng is.

By Dileep S

Oct 15, 2022

I am so happy that I took this course. Andrew is amazing, the way he teaches is great. Best course I have ever taken.

By naichuan z

Jul 16, 2022

I have learned Machine Learning in class, but this course gave me a more intuitive understanding of machine learning.

By ABHISHEK L

Jul 9, 2022

course gives in depth understanding of codes running in background and that too in a very easy to understand methods.

By Shivani C

Oct 14, 2024

I loved the teaching style of Andrew Ng and also the lab material prepared for this course. Really enjoyed learning!!

By Marcelo C

Feb 26, 2024

This is an excellent certification! I recommend it to anyone working on predictive analytics, ML classification, etc.

By Ruhama A

Jan 28, 2024

I enjoyed the coursework and learned the intuition behind the core concepts of linear and logistic regression models.

By Md. A S

Dec 13, 2023

Excellent foundation course to understand the core basics and equations behind Supervised machine learning algorithms