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课程概述

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....

热门审阅

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.

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.

筛选依据：

4951 - Supervised Machine Learning: Regression and Classification 的 4975 个评论（共 5,928 个）

创建者 Hamza A

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Mar 2, 2023

Satisfied!

创建者 Xueqi W

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Jan 3, 2023

Thank you!

创建者 lily h

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Jan 2, 2023

excellent.

创建者 yuzhuobai

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Nov 26, 2022

Thank you!

创建者 Pavan k C N

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Oct 14, 2022

very good

创建者 Shanaaz A

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Sep 30, 2022

Amazing!!

创建者 PEDRO M A D O

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Aug 21, 2022

Fantastic!

创建者 Gonzalo M

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Jul 21, 2022

Very Good

创建者 Rui J ( S / N

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Jul 19, 2022

Very good!

创建者 Mislav P

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Jul 18, 2022

Fantastic!

创建者 Ibrahim H M A

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Jul 15, 2022

great work

创建者 Sagar K

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Jul 14, 2022

Thank You!

创建者 Shivansh M I I

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Oct 30, 2025

very good

创建者 Ahmed M

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Oct 14, 2025

Excellent

创建者 Gulsum

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Oct 10, 2025

Recommend

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Oct 4, 2025

very nice

创建者 Ahmed W

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Sep 18, 2025

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创建者 Alizon V

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Sep 16, 2025

EXCELENTE

创建者 Nicolás S (

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Sep 15, 2025

Muy bueno

创建者 Sunny J

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Aug 31, 2025

Excellent

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Aug 13, 2025

Very good

创建者 EDGARD A D S

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Aug 6, 2025

muito bom

创建者 AI E

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Aug 3, 2025

Excellent

创建者 R. H G

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Jun 27, 2025

excellent

创建者 Saikumar J

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Jun 14, 2025

very Good