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.
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: Model Evaluation
Status: Logistic Regression
IntermediateCourse20 hours
Featured reviews
FO
5.0Reviewed 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.
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!
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
FG
5.0Reviewed Aug 28, 2019
Very informative course, showing mostly how to use many different Machine Learning techniques. Although mathematical details are not discussed much, the intuition of the methods are discussed.
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.
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 !!
MA
5.0Reviewed Aug 31, 2020
This is an amazing course. specially for those with little knowledge with python. This course has helped with the basic understanding of Machine learning and usage of python as a data scientist.
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!
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.
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.