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Learner Reviews & Feedback for Machine Learning with Python by IBM

4.7
stars
17,862 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’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....

Top reviews

RV

Jan 14, 2025

good course , some part is typical more statistical part shown, even i have good understanding of ML , so new learner will find little typical. rest tutor voice and language is understandable.

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.

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2501 - 2525 of 3,169 Reviews for Machine Learning with Python

By ABU H M A R

Jan 5, 2020

Great course for the fundamentals of different machine learning techniques. Enjoyed this wuite a kot

By Manal C

Nov 27, 2019

Recommending more emphasis on the coding behind the algorithm (reminders / links to references ...)

By Matthew A

Jun 14, 2019

This was a good course - but still could use more hands-on exercises to go along with the lectures.

By 018 A D

May 19, 2020

it was a nice course but there must be a little explanation video of the codes written in the labs

By Brian G

Jun 17, 2019

Would've liked the labs to be a little less demo and more DIY, but otherwise outstanding material.

By david m

Feb 23, 2025

The course was good however at some Point, labs started feeling like advertising for IBM's SNAPML

By Mitchell H

May 15, 2020

Covers all the basics of sklearn library. Would have been nice to have more assignments/practice.

By Joseph L

Jul 24, 2019

I am enjoying the course so far. Very well explained with a pretty comprehensive course material.

By nilay m

Mar 11, 2019

it was a nice course giving basics of every ML algorithm and i am all in all very much benefited.

By Saptashwa B (

Mar 4, 2019

The course is pretty good, I just hope the printing mistakes in the slides will be corrected soon

By ADEJOKUN A

Apr 27, 2020

The underlying concepts of the various algorithms were broken down and delivered with simplicity

By Mahesh W

Apr 18, 2025

Very disappointed the slides are not available for download, resulting in a lot of wasted time.

By Bala M

Sep 17, 2019

Excellent course explaining all the essential details to kick of the ML journey using Python.

By Lovish G

Apr 26, 2024

Difficult to understand for a Non-mathematical student. Overall a great learning experience.

By Neil C

Aug 29, 2020

It was a good course but the final exam could do with more structure around what is expected

By Urs H

Jun 29, 2019

Comprehensive, good to understand, minor errors in the description of the final assignḿent.

By Alex L

Oct 18, 2022

Very eductational as a intro to machine learning - wish the homeworks were more difficult

By rk s

Feb 20, 2022

the final peer review quiz was too difficult, should have included more practice exercises

By Ciniso M

May 25, 2021

This course is very fruitful and it's instructors are awesome. Thank you Coursera and IBM.

By Mitanshi K

Apr 30, 2020

Great for beginners. Explains theoretical concepts well but lags on the coding part of it.

By Vinit K S

Apr 8, 2020

It is such a vast topic, It would have really been great if there were few more exercises.

By Ritesh P

Dec 4, 2023

Random Forest, XGBoost etc should have been there. Decision tree explanation was amazing.

By Daniel J B O

May 26, 2020

A little bit to basic for someone who studied the topics in the past but a good refresher

By Mauricio C

Jul 11, 2024

Hace falta que se explique mas los cuadernos de laboratorio para entender mas la teoria

By Rubén G

May 29, 2020

I recommend including more examples and documentation of the metrics in the algorithms.