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

Access provided by Societe Generale GSC

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.

Learn new concepts from industry experts
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.