Introduction- Types - Applications - Tools in machine learning - Types of data - Exploring structure of data - Data Quality – Remediation - Data preprocessing. Design and Analysis of Machine Learning experiments: Factors - Guidelines - Cross Validation and Resampling methods- Measuring classifier performance-Assessing classifier algorithm’s performance

Introduction to model – Model Selection: Predictive Model-Descriptive Model-Training a Model - Model representation, Interpretation – Evaluating performance of Model – Improving performance of a Model. Feature Engineering: Feature Transformation - Feature Subset Selection.

Introduction - examples - Regression algorithm: simple linear regression - Multiple linear regression - polynomial regression model - Logistic regression. - Classification Model- Classification learning -Classification algorithms: Naive Bayes - K-nearest Neighbour - Decision tree - Random Forest model - Support Vector Machine – Dimension reduction: PCA

Introduction to biological and artificial neuron – Activation functions –Architecture of neural network: Single layered feed forward ANN - Multilayered feed forward ANN-competitive network-Recurrent Network -Learning process in ANN- Back Propagation Deep Learning. Unsupervised Learning: Introduction –Applications – Clustering algorithms.

Reinforcement learning - Elements of Reinforce learning - Types of Reinforcement Learning. Representation Learning-Active learning –Instance based Learning – Ensemble Learning Algorithm - Regularization Algorithm.