Supervised Machine Learning: Regression and Classification
Master foundational machine learning concepts through hands-on Python implementation of regression and classification models.
Course Cost
Free course
Beginner
Skill Level
32 Hours
Self-paced lessons
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
What you'll learn
Build machine learning models using Python
Implement linear and logistic regression
Apply regularization to prevent overfitting
Use gradient descent optimization
Develop supervised learning algorithms
Skills you'll gain
This course includes:
5.8 Hours PreRecorded video
9 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate

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There are 3 modules in this course
This comprehensive course, taught by AI pioneer Andrew Ng, introduces fundamental supervised machine learning concepts. Students learn to implement linear regression and logistic regression models using Python, NumPy, and scikit-learn. The curriculum covers essential topics including gradient descent, feature scaling, polynomial regression, and regularization to prevent overfitting, with hands-on programming assignments and labs to reinforce learning.
Week 1: Introduction to Machine Learning
Module 1 · 7 Hours to complete
Week 2: Regression with multiple input variables
Module 2 · 9 Hours to complete
Week 3: Classification
Module 3 · 16 Hours to complete
Fee Structure
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
Frequently asked Questions
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