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.
Topics:
• 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
Skills:
• Supervised Learning concepts
• Implementing different types of Supervised Learning algorithms
• Evaluating model output