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Learner Reviews & Feedback for Machine Learning with Python by IBM

4.7
stars
17,941 ratings

About the Course

Python is a core skill in machine learning, and this course equips you with the tools to apply it effectively. You鈥檒l 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鈥檒l also explore unsupervised learning, including clustering methods and dimensionality reduction with PCA, t-SNE, and UMAP. Through real-world labs, you鈥檒l 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....

Top reviews

FO

Oct 9, 2020

I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.

RC

Feb 7, 2019

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

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2151 - 2175 of 3,187 Reviews for Machine Learning with Python

By vinayak s

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Oct 23, 2021

good

By Shreya M

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Oct 14, 2020

Great

By Purple M

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Oct 7, 2020

great

By Andrey K

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Jul 4, 2020

good!

By A S R

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May 28, 2020

super

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Apr 24, 2020

great

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Mar 19, 2020

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Jan 21, 2020

goood

By puorella 2

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Aug 13, 2019

Great

By 於旍榿欷�

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Jul 23, 2019

鞛鞛堨棃鞚�

By AYUSH K 3

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Sep 7, 2025

nice

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Aug 26, 2025

Good

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Aug 11, 2025

good

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Aug 11, 2025

good

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Aug 9, 2025

GOOD

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GOOD

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Jul 6, 2025

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Jun 23, 2025

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May 23, 2025

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May 9, 2025

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Apr 15, 2025

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Apr 10, 2025

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Apr 9, 2025

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By Hung P

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Apr 6, 2025

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By IV C 5

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Apr 1, 2025

Best