MODULE 5 – MACHINE LEARNING

1. Introduction to Machine Learning
2. Supervised Learning
3. Unsupervised Learning
4. Reinforcement Learning

Introduction to Machine Learning: Machine learning concepts and applications, overview of supervised, unsupervised, and reinforcement learning.

Supervised Learning:

• Regression analysis
• Linear regression
  • Simple & multiple linear regression
• Classification algorithms
  • Logistics regression
  • KNN (K-Nearest Neighbors)
  • Support Vector Machine
  • Naive Bayes
• Classification vs regression
• Ensemble techniques
  • Decision tree classification algorithm
  • Bagging
  • Random forest models

Unsupervised Learning:

• Hierarchical clustering in machine learning
• K-Means clustering algorithm
• APRIORI algorithm
• Confusion matrix
• Cross-validation
• Overfitting & underfitting
• Principal component analysis (PCA)
• Independent component analysis (ICA)
• Anomaly detection

Machine Learning Tools:

• Ensemble techniques
• Recommendation systems in machine learning
• Model evaluation

Reinforcement Learning:

• Introduction and basics of RL
• RL framework
• RL process
• Code standards and libraries used in RL (Python/Keras/TensorFlow)
• Types of RL
• The Bellman equation
• Markov decision process
• Applications of RL