Supervised Learning: Classification

Introduction

In this course, you will learn how to solve classification problems using various supervised learning algorithms.

🎯 Tasks

In this course, you will learn:

• How to implement logistic regression, K-nearest neighbor algorithm, naive Bayes, support vector machine, perceptron and artificial neural network, decision tree and random forest, and bagging and boosting methods.
• How to understand the principles behind each of these classification algorithms.
• How to implement and apply these algorithms to solve real-world classification problems, such as handwritten digits recognition.

🏆 Achievements

After completing this course, you will be able to:

• Understand the strengths and weaknesses of different classification algorithms and choose the appropriate one for your problem.
• Implement and apply these algorithms to solve classification problems in various domains.
• Evaluate the performance of these algorithms using cross-validation techniques.