What is regression in AI?

Regression in AI involves modeling the relationship between variables to predict continuous outcomes, such as housing prices or stock trends, using algorithms like linear regression.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Supervised Learning Regression Classification Clustering

Supervised Learning Regression Classification Clustering

This course is part of AI ML with Deep Learning and Supervised Models Specialization

Instructor: Simplilearn Instructor

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Master linear and logistic regression techniques
Apply Decision Trees, Random Forest, and Naive Bayes models
Use K-Means Clustering for data segmentation
Solve real-world problems with machine learning methods

Skills you'll gain

Tools you'll learn

Classification Algorithms

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

2 assignments

Taught in English

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This course is part of the AI ML with Deep Learning and Supervised Models Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

This comprehensive Supervised and Unsupervised Machine Learning program will equip you with essential skills for data modeling and analysis. You’ll master regression techniques, classification models, and clustering algorithms to address real-world challenges and drive impactful data solutions.

By the end of this course, you will be able to: - Master Regression Techniques: Learn linear and logistic regression to predict variables and classify data, and select the right method for your projects. - Apply Classification Models: Gain expertise in Decision Trees, Random Forest, and Naive Bayes for accurate data analysis and predictions. - Implement Clustering Algorithms: Understand and apply K-Means Clustering to identify patterns, group data, and solve tasks like segmentation and recognition. - Solve Real-World Problems: Use supervised and unsupervised learning techniques to tackle complex challenges and make data-driven decisions. Guided by experts, you’ll acquire practical skills to excel in machine learning and deliver innovative solutions across industries.

This Supervised and Unsupervised Machine Learning program covers essential techniques for data modeling and analysis. Start with regression analysis, mastering linear regression for continuous variable prediction and logistic regression for binary classification. Learn to select the best approach for your projects. Explore classification models, including Decision Trees for data splitting, Random Forest for robust predictions, and Naive Bayes for probabilistic classification. Gain practical skills to apply these methods in real-world scenarios. Dive into unsupervised learning with the K-Means Clustering algorithm, understanding how it groups data into clusters based on similarities. Apply it to challenges like market segmentation and image compression. This program equips you with essential machine learning skills for impactful data solutions.

What's included

25 videos3 readings1 assignment

25 videos Total 254 minutes

Types of Regression in Supervised Learning 7 minutes
What is Linear Regression? 10 minutes
Linear Regression 15 minutes
Multiple Linear Regression 15 minutes
Use Case Implementation of Linear Regression 10 minutes
Logistic Regression 10 minutes
Use Case Implementation 17 minutes
Classification Models in Supervised Learning 8 minutes
Demo on Logistic Regression Part - 1 11 minutes
Demo on Logistic Regression Part - 2 3 minutes
Demo on K-Nearest Neighbors 12 minutes
Demo on Support Vector Machines 9 minutes
Decision Tree Tutorial 8 minutes
Demo on Decision Trees 6 minutes
Use Case - Loan Repayment Prediction 6 minutes
Advantages of Decision Tree 7 minutes
Decision Tree in Machine Learning 14 minutes
Use Case Implementation Part 1 11 minutes
Random Forest Algorithm 14 minutes
Use Case Implementation Part 1 11 minutes
Use Case Implementation Part 2 8 minutes
What is Naive Bayes? 6 minutes
Understanding Naive Bayes Classifier 14 minutes
Advantages of Naive Bayes Classifier 9 minutes
Use Case Implementation 14 minutes

3 readings Total 30 minutes

Course Syllabus 10 minutes
Types of Regression in Supervised Learning 10 minutes
Classification Models in Supervised Learning 10 minutes

1 assignment Total 110 minutes

Assessment for Supervised Learning – Regression and Classification 110 minutes

Explore clustering techniques, focusing on K-Means, its applications, and real-world use cases.

What's included

7 videos1 reading1 assignment

7 videos Total 50 minutes

Types of Clustering 3 minutes
What is K Means Clustering? 7 minutes
Applications of K-Means Clustering 3 minutes
How Does K-Means Clustering Work? 8 minutes
K-Means Clustering Algorithm 6 minutes
Demo K-Means Clustering 12 minutes
Use Case Color Compression 11 minutes

1 reading Total 10 minutes

K Means Clustering Algorithm 10 minutes

1 assignment Total 10 minutes

Assessment for Unsupervised Learning – Clustering Algorithms 10 minutes

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Instructor

Simplilearn Instructor

Simplilearn

23 Courses 24,072 learners

Offered by

Simplilearn

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Frequently asked questions

Regression predicts continuous outcomes (e.g., sales forecast), classification assigns data into categories (e.g., email spam detection), and clustering groups data based on similarities (e.g., customer segmentation).

A machine learning course can vary in duration, typically lasting from a few weeks for beginner-level programs to several months for comprehensive or advanced courses.

Clustering is an unsupervised learning technique in AI that groups similar data points into clusters, helping to uncover patterns and insights, such as segmenting customers by behavior.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.