Schedule
Asynchronous
Delivery method
Online
Machine Learning with Python
0 credit hours
Credits awarded upon completion
Self-Paced
Progress at your own speed
20.04 hours
Estimated learning time
About the Course
Description
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.
This Course is part of a program
You can only buy it along with program.
Sections
Deliverables
0 Credits
Academic Excellence
Earn necessary number of credit hours for completing this content
Hone Important Skills
Total Upgrade
Such as Machine Learning, Regression Analysis, Supervised Learning, Unsupervised Learning, Dimensionality Reduction, Predictive Modeling, Data Validation, Applied Machine Learning, Scikit Learn (Machine Learning Library), Feature Engineering, Jupyter, Python Programming, Performance Tuning, Statistical Modeling, Classification And Regression Tree (CART)