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

4.9
stars
32,127 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

DB

May 30, 2024

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

AA

Apr 29, 2023

Optional Lab lot more time than mentioned without prior experience of python and libraries used. Its estimated time should be change, it's a lot more than 1 hour. Video and exercises are very good.

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4951 - 4975 of 6,058 Reviews for Supervised Machine Learning: Regression and Classification

By Carlos R d S R

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May 2, 2025

Ótimo curso

By john_ d

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Apr 16, 2025

exceptional

By MD. N H J S 2

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Apr 4, 2025

Best course

By Mudit C

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Mar 23, 2025

Amazing !!!

By ASHWANTH S K C

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Jan 22, 2025

best course

By Aneesh P

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Dec 22, 2024

Really good

By Pradeep Y

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Dec 8, 2024

very helpul

By Prathmesh K

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Nov 18, 2024

best course

By Hamza S

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Nov 4, 2024

Good course

By Prathamesh P

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Oct 31, 2024

Nice cource

By Azlan M

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Oct 11, 2024

very helful

By Anne S

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Sep 23, 2024

Exceptional

By CHITTA A

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Sep 22, 2024

Interesting

By MEEGADA S R

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Aug 17, 2024

nice course

By Gabriel V M

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Jul 30, 2024

Excellent!!

By Asad H

•

Jul 17, 2024

Good course

By Amir R M F

•

Jul 7, 2024

Excellent!!

By Jonh V A

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Jul 1, 2024

good course

By SAMEER T (

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Jun 3, 2024

good course

By sachin k

•

Mar 12, 2024

good course

By Giancarlo V

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Jan 30, 2024

Beautiful!!

By Zain U A

•

Jan 24, 2024

Best Course

By sohaib

•

Jan 6, 2024

nice course

By Nilesh P

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Dec 17, 2023

Best course

By Jorge S

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Nov 26, 2023

Really cool