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学生对 DeepLearning.AI 提供的 Supervised Machine Learning: Regression and Classification 的评价和反馈

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32,078 个评分

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

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.

筛选依据：

4501 - Supervised Machine Learning: Regression and Classification 的 4525 个评论（共 6,053 个）

创建者 Huỳnh T T

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Apr 16, 2023

Good for beginners

创建者 Ashutosh B

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Mar 13, 2023

Best for beginner.

创建者 Minh T D

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Feb 28, 2023

Great introduction

创建者 SADEEM A N A

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Dec 12, 2022

good Learning

👍🏻

创建者 Mahdi F

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Dec 10, 2022

Awesome and useful

创建者 Dat V N

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Oct 16, 2022

Good for beginners

创建者 Akshay M

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Sep 28, 2022

Very insightful .

创建者 Pragyansh S

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Sep 20, 2022

wonderful course.

创建者 Madhu O

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Sep 6, 2022

I enjoyed course!!

创建者 Vinay C

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Jul 29, 2022

Very well explain.

创建者 Joaquin V

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Jul 6, 2022

Easy to understand

创建者 Leon Q

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Jun 29, 2022

Lab is very useful

创建者 Killian P

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Jun 22, 2022

Very well taught !

创建者 Md. R M ,

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Feb 3, 2026

Very interactive.

创建者 SIDDHARTH P

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Dec 2, 2025

Best one to exist

创建者 Amr K

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Nov 29, 2025

excellent content

创建者 Cochet F

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Oct 28, 2025

Andrew the goat !

创建者 VAIBHAV S

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Oct 15, 2025

very descriptive.

创建者 Phil C

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May 29, 2025

Excellent design.

创建者 Bằng L V

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May 24, 2025

it's good program

创建者 Thanh L N

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Apr 23, 2025

very good!!!!!!!!

创建者 Tuhin D

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Mar 12, 2025

Excellent course!

创建者 BIGYAN R

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Mar 3, 2025

beginner friendly

创建者 Lavanya

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Feb 13, 2025

Excellent lecture

创建者 Ankit D

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Jan 27, 2025

Apt and Concise !