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学生对 IBM 提供的 Machine Learning with Python 的评价和反馈

4.7
18,298 个评分

课程概述

Python is a core skill in machine learning, and this course equips you with the tools to apply it effectively. You’ll learn key ML concepts, build models with scikit-learn, and gain hands-on experience using Jupyter Notebooks. Start with regression techniques like linear, multiple linear, polynomial, and logistic regression. Then move into supervised models such as decision trees, K-Nearest Neighbors, and support vector machines. You’ll also explore unsupervised learning, including clustering methods and dimensionality reduction with PCA, t-SNE, and UMAP. Through real-world labs, you’ll practice model evaluation, cross-validation, regularization, and pipeline optimization. A final project on rainfall prediction and a course-wide exam will help you apply and reinforce your skills. Enroll now to start building machine learning models with confidence using Python....

热门审阅

CA

Dec 31, 2019

could be split in two courses to be given enough focus. it was very condensed and needed more time and explanation in each section. The instructor was very good but more details would have been nice

RN

May 25, 2020

Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!

筛选依据:

826 - Machine Learning with Python 的 850 个评论(共 3,260 个)

创建者 Harold C

Sep 15, 2022

Very detailed course with lots of hands-on exercises. I learned and enjoyed a lot.

创建者 Hemant S

Apr 20, 2021

Best for anyone who is not from Computer Science Background and looking to learn ML

创建者 Vedang

Dec 19, 2020

Exceptional course to understand the basics of supervised and unsupervised learning

创建者 T A

Nov 19, 2020

great course put me on the way to master machine learning ,,, i learned many things

创建者 CHALLA K S N M S

Aug 24, 2020

Awesome experience.Thanks to my coaches Saeed Aghabozorgi and Joseph Santarcangelo.

创建者 Mouafo D

Jun 28, 2020

From nothing, I ended up getting the foundation of machine programming with python.

创建者 Eva B

May 23, 2020

Top class from 9-series! Though for beginners I reccormend to pass previous classes

创建者 Josh G

May 1, 2020

Thank you so much for making the basic concepts and real-world tasks easy to learn!

创建者 John L

Aug 16, 2019

Very interesting and informative. Instructor's explanations are clear and helpful.

创建者 Vallian S

May 5, 2022

It is very good for beginner since it shows enough general ideas of every ML type

创建者 Antonio P

Jun 30, 2021

Best course so far! This is a perfect introduction to machine learning algorithms.

创建者 Rahul G

Sep 5, 2020

Excellent course to understand the knowledge of machine learning as a new learner.

创建者 SK A R

Apr 27, 2020

This course is very helpfull for aspirants of python language in Machine Learning.

创建者 Retnani L

Mar 29, 2020

it's easy to follow for those who want to learn machine learning in beginner level

创建者 Brett R

Oct 23, 2019

Great course. Course content was excellent, especially regarding model evaluation.

创建者 Arisara C

Sep 7, 2018

Machine Learning is to make the computer system learn by itself using information.

创建者 Roshan N

Nov 22, 2024

the course is good for understanding the concept of machine learning from scratch

创建者 BRUNO P G

May 2, 2024

CURSO MUITO BOM E COMPLETO. APÓS O ESTUDO, NÃO HÁ NENHUMA DÚVIDA SOBRE O ASSUNTO.

创建者 Diego N

Oct 3, 2022

Great for fundations. I recommend +hours of practice on each week's assignments.

创建者 Muhammad o

Jul 23, 2022

A very useful and helpful course, I have learned a lot, thank you to the IBM team

创建者 João R

Sep 18, 2020

Great introduction to Machine Learning with real-world problems. Really liked it.

创建者 Edouard T

Sep 14, 2020

amazing course. I just wish it's updates to a pyhton/jupyter coding environement.

创建者 RaviTeja P

Aug 3, 2020

Nice Course explained in a short and simple ways all machine learning algorithms.

创建者 Manula V

Apr 30, 2020

Excellent course with important concepts of the algorithms explained very simply.