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!
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: Unsupervised Learning
Status: Regression Analysis
IntermediateCourse20 hours
Featured reviews
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.
CA
4.0Reviewed Dec 31, 2019
could be split in two courses to be given enough focus. it was very condensed and needed more time and explanation in each section. The instructor was very good but more details would have been nice
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.
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.
TG
4.0Reviewed Sep 24, 2020
Excellent course for beginners to data science field. Would have been better if the final project also included flavor of other ML methods such as Regression, Clustering or Recommender Systems.
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!
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.
NN
5.0Reviewed Dec 29, 2019
This is a very good start for Machine leaning with Python. I didnt have much idea about ML concepts but this course gave me great understanding on each topic and lot of learning. Awesome Course !!
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.
JL
5.0Reviewed Dec 5, 2018
I am happy to have this online education, I drop out my nuclear engineering degree, I am happy to learn practical things with future... I work for IBM also...but I want to become a data scientis
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).