RV
5.0Reviewed 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.
Machine Learning with Python
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.
Status: Statistical Machine Learning
Status: Model Optimization
IntermediateCourse20 hours
Featured reviews
RN
5.0Reviewed May 25, 2020
Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!
AG
5.0Reviewed Feb 1, 2020
Quite an informative course, well presented material without being overbearing for newcomers to ML. Highly recommended to everyone with prior CS experience who wants to get into AI/ML workloads.
MJ
5.0Reviewed Jun 3, 2020
In peer graded assignments, if someone is grading any peer below passing criteria then it must be compulsory to let the learner know his mistakes or shortcomings because of which he does not graded.
AJ
5.0Reviewed Jul 8, 2019
This was a very informative course. The videos provided a good background on the concepts and I found the labs especially helpful for learning to implement Python code for each technique covered.
RC
5.0Reviewed 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.
ND
5.0Reviewed Jul 15, 2020
This course is a great way to start learning about ML, as it sets out what you need to do step-by-step, explains very clearly why, and gives you a chance to experiment and practise. Thank you IBM!
IK
5.0Reviewed Dec 13, 2022
Thank you Coursera & IBM for offering such a wonderful career-oriented course. Thank you very much Dr SAEED AGHABOZORGI and Dr Joseph Santarcangelo for providing the amazing learning Journey.
SS
5.0Reviewed Apr 16, 2020
I liked it very much and was able to clearly understand the usage in programming language with ML related libraries. Thanks to IBM friends and Coursera for providing the expertise and the platform.
FF
5.0Reviewed Dec 9, 2021
Great course, they teach the very basic steps for data analysis world, which is awesome so we can get a solid basics understanding, the tests are in a great level (neither too easy nor too hard).
SK
5.0Reviewed Aug 3, 2019
The instructor was awesome. His voice was crisp and to the point. The course is actually well laid out with proper structure. Altogether a great learning experience. Cheers... Keep up the good work.
JT
5.0Reviewed Apr 17, 2020
This course was a great taster for machine learning techniques. My only recommendation would be to add more explanation on tuning techniques for models and cover more of the supporting mathematics.