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Learner Reviews & Feedback for Machine Learning with Python by IBM

4.7
stars
16,374 ratings

About the Course

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning. This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency....

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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Aug 25, 2024

good

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May 8, 2024

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Jan 17, 2024

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By RAWAN T F A

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Nov 11, 2023

cool

By RAMJI R (

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Oct 17, 2023

good

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Oct 17, 2023

Good

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Sep 19, 2023

nice

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Sep 1, 2023

well

By Umar M Y

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Jul 12, 2023

100%

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May 29, 2023

good

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Apr 13, 2023

good

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Jan 26, 2023

nice

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Jan 24, 2023

good

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Dec 29, 2022

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Sep 8, 2022

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Sep 5, 2022

good

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Aug 10, 2022

Good