Ementorhub

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

Machine Learning Algorithms

Data Modeling

Regression Analysis

Classification And Regression Tree (CART)

Data Analysis

Predictive Analytics

Unsupervised Learning

Bayesian Statistics

Predictive Modeling

Random Forest Algorithm

Supervised Learning

Machine Learning

There are 2 modules in this course

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.

Unsupervised Learning – Clustering Algorithms