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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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401 - 425 of 2,850 Reviews for Machine Learning with Python

By Antonio M G

Feb 10, 2021

Very very instructive and excellent driven for learning the most well-known Machine Learning algorithms using Python. I would strongly recommend it

By Alaa F

Jan 24, 2021

Very well structured, Jupyter notebooks are provided as a support including code and explanation/summaries of the used algorithms. Highly recommend.

By Mallikarjun K

Oct 12, 2020

Best Machine Learning Course!.

But make sure to learn basics, data visualization, and data manipulation with python before enrolling in this course.

By Gabriella T

Oct 6, 2020

This course allowed me to implement the already-known machine learning algorithms with Python language which I didn' t know! This helped me a lot!!!

By Shoaib M

Jun 15, 2020

This course has helped me a lot in clearing the doubts regarding Machine Learning algorithms. I strongly recommend this course to every ML beginner.

By Federico N

May 22, 2020

Well done and structured work! I strongly suggest this course. The main teacher is really skilled at explaining such a complex topic in an easy way.

By Krishna M

Mar 29, 2020

This is one of the best course which I have taken. The basics were explained very greatly, and also it is very simple to understand the course work.

By Mohammed E

Nov 6, 2019

Really good and knowledgeable professor. Very well put together presentations and labs. Everything is just perfect. I highly recommend this course.

By Tirth R

Feb 14, 2021

It would more amazing if the instructor would share snippets and explain the code along in ppt rather than making a separate lab for implementation

By Isabelle W

Nov 4, 2022

Great course, I found it to be very engaging and it gives you the opportunity to learn, and practice what you have learned in exercises and exams

By SEONGJIN C

Nov 17, 2020

The course contents were just right to follow easily, and practices were good enough to learn new skills. Thanks to the instructors and the team.

By Abhishek P G

Jul 7, 2020

The course has introduced me to various tools that are very useful to my training. I could explore using them to make my lesson more interactive.

By Pankaj S

Mar 12, 2020

It was fantastic learning experience knowing some many traditional machine learning statistical tools and the mathematics behind these algorithms

By Parisa S

Jul 23, 2019

Great course! Enjoyed both videos and practice labs. Strongly recommended for people who know Python and want to get started on machine learning.

By Amrizal S D b A S

Jun 4, 2023

Really really good training and notes and the speaker are very good at explaining these topics. Recommended that others try to seek this course.

By Анастасия Л

Apr 22, 2022

Probably the best course in the specialization. I really enjoyed the labs. A lot of useful information. Many thanks to the authors of the course

By Juan M H T

Jan 9, 2021

Great introduction to Machine Learning algorithms. Hands-on labs are perfect to see them in action and the whole content is very well explained.

By Sundas L

Jun 15, 2020

I would recommend data science enthusiasts to take this course. I got a deep understanding of machine learning by working on real-life datasets.

By Georgios L

May 3, 2020

This is the best course in the IBM Data Science series, the course teacher is very good in communicating principles and explaining what matters.

By Phạm T S

Sep 7, 2019

This course is great, helping me master the knowledge regarding algorithms in machine learning.

Thank you very much Professor SAEED AGHABOZORGI.

By RICHARD D

Feb 12, 2020

This Is One of the best Course In Machine learning so far. Tutorial Very Brief and practical. Very interactive lab. Thanks to Coursera and IBM.

By Tareq A

Mar 30, 2019

Really its fully rich course, explains every aspect of machine learning algorithms and their implementation in Python Language.

Thank You at all

By Ssentumbwe E

Apr 17, 2020

The first time I tried to learn machine learning, it scared me. This course made is the best introduction to the subject that I have ever got.

By Victor B D

Apr 1, 2020

this was one of the most difficult courses I took and it was very worth all the content and very good and the exercises are very well prepared

By Robert

Feb 7, 2020

An excellent course, covering multiple machine learning algorithms which allow you to compare and contrast their strengths and methodologies.