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Supervised Machine Learning: Regression and Classification
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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.

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4.9

7,54,477 Enrolled

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English

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olive-leaves-logo

4.9

7,54,477 Enrolled

olive-leaves-logo

English

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

Machine Learning
Linear Regression
Logistic Regression
Python Programming
NumPy
Scikit-learn
Supervised Learning
Feature Engineering
Model Training
Gradient Descent

This course includes:

5.8 Hours PreRecorded video

9 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

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Certificate

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Share your certificate with prospective employers and your professional network on LinkedIn.

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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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

Reviews

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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.

Supervised Machine Learning: Regression and Classification

Beginner

Skill Level

32 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.