UNIT 04 CLASSIFICATION ALGORITHMS IN MACHINE LEARNING
This module introduces classification algorithms, different types of learners, and major classification approaches used in Machine Learning. It covers model evaluation, the SoftMax function, key performance metrics, and practical use cases for applying classification algorithms to real-world prediction and decision-making problems.
Module Overview
This module introduces classification algorithms, different types of learners, and major classification approaches used in Machine Learning. It covers model evaluation, the SoftMax function, key performance metrics, and practical use cases for applying classification algorithms to real-world prediction and decision-making problems.
What you will learn here
This course introduces the fundamentals of Machine Learning, including data types, data preprocessing, regression, classification, clustering, ensemble learning, Support Vector Machines, Decision Trees, K-Nearest Neighbours, Naïve Bayes, Random Forest, and Reinforcement Learning. It equips learners with practical skills to prepare data, build and evaluate Machine Learning models, implement algorithms using Python, and develop effective real-world predictive solutions.
How to access
Learn at your ownStart and pause anytime.
Accessible on all devicesLearn on mobile, tablet or desktop.
