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Learner Reviews & Feedback for Machine Learning with Python by IBM

4.7
stars
17,190 ratings

About the Course

Python is one of the most widely used programming languages in machine learning (ML), and many ML job listings require it as a core skill. This course equips aspiring machine learning practitioners with essential Python skills that help them stand out to employers. Throughout the course, you’ll dive into core ML concepts and learn about the iterative nature of model development. With Python libraries like Scikit-learn, you’ll gain hands-on experience with tools used for real-world applications. Plus, you’ll build a foundation in statistical methods like linear and logistic regression. You’ll explore supervised learning techniques with libraries such as Matplotlib and Pandas, as well as classification methods like decision trees, KNN, and SVM, covering key concepts like the bias-variance tradeoff. The course also covers unsupervised learning, including clustering and dimensionality reduction. With guidance on model evaluation, tuning techniques, and practical projects in Jupyter Notebooks, you’ll gain the Python skills that power your ML journey. ENROLL TODAY to enhance your resume with in-demand expertise!...

Top reviews

FO

Oct 8, 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 6, 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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2676 - 2700 of 3,008 Reviews for Machine Learning with Python

By Cristian C P Á

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Nov 14, 2019

Good!

By Srijam S

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Apr 25, 2025

good

By Prince K

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Apr 23, 2025

Nice

By PUSHPRAJ J

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Apr 13, 2025

good

By Sungyong P

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Feb 3, 2025

good

By kanimozhi g

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Sep 14, 2024

good

By Abdoulaye W D

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Oct 7, 2023

GOOD

By Muqseet F

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Apr 17, 2023

good

By Girija S M

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Aug 23, 2022

nice

By A R

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Feb 19, 2022

good

By Anshuman R

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Jul 15, 2021

good

By Mullangi T

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Jun 21, 2021

GOOD

By SHALINI S

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Sep 6, 2020

Good

By Zakir H

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Jul 19, 2020

Good

By Sudhanshu R

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Jun 12, 2020

good

By Tejas S

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Apr 28, 2020

good

By VIGNESHKUMAR R

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Dec 26, 2019

Good

By Lakshmi N

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Dec 10, 2019

Good

By lokesh s

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Jul 17, 2019

good

By Hiep D X

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Oct 18, 2022

ok

By syed s

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Aug 8, 2021

wow

By piyush s

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May 19, 2020

ok

By Pagadala G s

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May 18, 2020

Ok

By RABAB E

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Dec 14, 2023

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By Malte H

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Jan 11, 2021

PRO: Good overview and basic introduction of common machine learning techniques.

CON:

- The final assignment is peer reviewed! I saw no mention of this before purchasing the course. This means you are at the mercy of other students who may have less experience than you and may notbe qualified in grading assignments. Also it may mean you have to wait a long time before you get your certificate. It would be better to implement a Kaggle-style assessment of the models and use that to obtain a score and turn that into a grade. This would be transparent and instantaneous.

Some of the forum answers provided by the teaching staff are half baked and often inconsistent. e.g. they give example code for making a figure and and also a figure. But the figure is obviously not made with the provided code and the code contains typos. This is frustrating and makes learning harder than it should be.

Some of the code in the lab exercises don’t obey good practices. e.g. in every lab the data is normalised before train/test splitting. In the final project there is a comment that this should be done the other way around (and it really should!). Why not do it the right way in all the examples throughout the course?