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Learner Reviews & Feedback for Machine Learning with Python by IBM

4.7
stars
15,281 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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2051 - 2075 of 2,670 Reviews for Machine Learning with Python

By Ana D

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Apr 13, 2020

I found the quality of teaching to be of good standard, however setting up IBM Watson studio and creating a project was very inconvenient and took a lot of time.

By Satyapriya R

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Mar 17, 2020

Well designed course for clear understanding of ML algorithms. Practical Labs gives you hands on experience to try out various algorithms. A very helpful course.

By Snow P

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Jan 18, 2021

Good part: This course provide a lot of concrete designed materials.

Not very good part: the content of videos are too simple, without any theoretical analysis.

By Tichaona M

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Oct 19, 2020

This course is great but one needs more time to read through other information sources. An in-depth understanding is critical to get the bigger picture of use.

By BYOUNG J Y

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Mar 6, 2019

Pros. Good for running code in Jupiter Notebook environment.

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Cons. Language Support only English (others are few), and Screen sub overlays presentation text.

By ROHIT K S

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Mar 10, 2019

Excellent course . Concepts of machine learning algorithm were explained clearly and easily.

The interface with IBM notebook could have been much smoother ,

By VARUN S

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Oct 25, 2019

Would like to say that it would be of great help if we had some more practice on coding. But overall a wonderful course and helped me learn a lot. Thanks!

By Hamsavardhini A

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Aug 16, 2021

It was an excellent course. They covered all topics of ML. Practical session was good but need more explanation. That would be very helpful to students.

By Anas Z O

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Nov 2, 2022

overall the course is very good and covered the topics in ML, but the coding examples should be explained in a videos to clarify all points in details.

By Nermin K U

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Nov 28, 2019

The final project is ill-structured. It is hard to grade because you need to go back and forth in the codes. It makes both doing and grading harder.

By Aftab R

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Jan 28, 2020

The course appears to assume good competency in Python and does not provide much training on Python. This should be highlighted to students upfront.

By Richa S

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Jan 12, 2023

I am new to Machine Learning , as anew student I find the course simple to understand. I need to work on my lab skills which I will finish slowly.

By Sherbulandkhan B

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Apr 26, 2020

Course is very well structured. Some extra guidance and assistance would be nice with the Peer-graded assignment as it gets bit tricky and complex.

By Luis D C

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Jan 23, 2020

Learned a lot in this course, I would've liked there were more exercises throught the videos rather than some questions at the end of the section.

By Hakan D

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Jul 6, 2020

There were a couple of videos where the notes weren't separated with punctuations. But other than that, it was a really good course. Thank you.

By João P d J S d R

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Oct 29, 2020

This course is very well, but it doen't have model selection and stratified features selection with sklearn.model_selection.train_test_split.

By Tarit G

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Nov 29, 2019

It was an awesome experience to learn machine learning. The instructor has explained every algorithm in a detailed way. It was very helpful.

By Denise N

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Apr 22, 2023

good for an introduction to machine learning. the material could have been a lot more deeper on the various algorithms and when it is used.

By Luke P

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Jan 25, 2021

Good course if you have some basic knowledge of Python and data analysis. However, much of the course material had typos and small errors.

By Laura S M D

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Dec 14, 2019

Un curso muy completo, aunque mejoraría un poco los ejercicios, que al estudiante se le diera más importancia en la resolución del programa

By Jacqueline ( G

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Aug 4, 2019

It's so bad when someone reviews your assignment and gives you an unfair score. But this happened a lot because of this peer review system.

By Muhammad R F D

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Mar 4, 2020

Well Explained. Video lecs are very easy to understand and upto the mark...Assignments little bit need more clarification and explanation.

By Manoj S H

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May 4, 2023

I needed the syntax to be explained in the video tutorial also because it would be even easier to make the notes on a specific algorithm.

By Luis R

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Dec 19, 2021

Great course ! I really liked the fact that you don't need to install anything to try out the code and the system works without problems.

By Gaurav S

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Jul 19, 2019

The Course Could have been a little better if there were more theory and more illustrations at time a disconnect was felt in the Course