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.
Supervised Learning Regression Classification Clustering
Supervised Learning Regression Classification Clustering
Instructor: Simplilearn Instructor
Sponsored by Coursera Learning Team
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
7 hours to complete
3 weeks at 2 hours a week
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
1 assignment
Taught in English
Recently updated!
January 2025
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There are 2 modules in this course
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
32 videos3 readings
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
1 assignment
Instructor
Offered by
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