Module 5 – Machine 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 ML
- K-Means clustering algorithm
- KNN (K-nearest neighbors)
- Apriori algorithm
- Confusion Matrix
- Cross-validation
- Overfitting & Underfitting
- Principal Component Analysis
- Independent Component Analysis
- Anomaly Detection
- ML Tools:
- Ensemble techniques
- Recommendation systems in ML
- 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