Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

Objective: At the end of this module, you should be able to:
• Understand What is Naïve Bayes Classifier
• How Naïve Bayes Classifier works?
• Understand Support Vector Machine
• Illustrate How Support Vector Machine works?
• Hyperparameter optimization

Topics:
• What is Naïve Bayes?
• How Naïve Bayes works?
• Implementing Naïve Bayes Classifier
• What is Support Vector Machine?
• Illustrate how Support Vector Machine works?
• Hyperparameter optimization
• Grid Search vs Random Search
• Implementation of Support Vector Machine for Classification

Hands On:
• Implementation of Naïve Bayes, SVM