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Supervised Learning Regression Classification Clustering

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Supervised Learning Regression Classification Clustering

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Beginner level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace
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

Details to know

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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
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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
23 Courses•28,058 learners

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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.

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.

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.

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.

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.

Financial aid available,