Supervised Learning – I

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

• What are Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?

Hands On/Demo:
• Implementation of Logistic regression
• Decision tree
• Random forest

• Supervised Learning concepts
• Implementing different types of Supervised Learning algorithms
• Evaluating model output