About the course

Unit 1
Introduction to Machine Learning- Basic concepts, developing a learning system, Learning Issues, and challenges. Types of Machine Learning. Feature Selection Mechanisms, Imbalanced Data, Bias in Data, Outlier Detection

Unit 2
Supervised Learning- Linear Regression, Multiple Regression, Logistic Regression, Classification; Classifier Models, K Nearest Neighbor (KNN), Naive Bayes, Decision Trees, Support Vector Machine (SVM), Random Forest

Unit 3
Unsupervised Learning- Dimensionality Reduction; Clustering; K-Means Clustering; C-Means Clustering; Fuzzy C Means Clustering, Association Analysis- Association Rules in Large Databases, Apriori Algorithm, Markov Models: Hidden Markov Models (HMMs).

Unit 4
Reinforcement Learning- Introduction to Reinforcement Learning, Elements of Reinforcement Learning, Approaches to Reinforcement Learning, Applications of Reinforcement learning.
Applications of Machine Learning in different sectors: Medical Diagnostics, Fraud Detection, Email Spam Detection