1.Introduction to Machine Learning : Overview of Human Learning and Machine Learning, Types of Machine Learning, Applications of Machine Learning, Tools and Technology for Machine Learning. (Chapter - 1) 2.Preparing to Model : Machine Learning activities, Types of data in Machine Learning, Structures of data, Data quality and remediation, Data Pre-Processing: Dimensionality reduction, Feature subset selection. (Chapter - 2) 3. Modelling and Evaluation : Selecting a Model: Predictive/Descriptive, Training a Model for supervised learning, model representation and interpretability, Evaluating performance of a model, Improving performance of a model. (Chapter - 3) 4. Basics of Feature Engineering : Feature and Feature Engineering, Feature transformation: Construction and extraction, Feature subset selection : Issues in high-dimensional data, key drivers, measure and overall process. (Chapter - 4) 5.Overview of Probability : Statistical tools in Machine Learning, Concepts of probability, Random variables, Discrete distributions, Continuous distributions, Multiple random variables, Central limit theorem, Sampling distributions, Hypothesis testing, Monte Carlo Approximation. (Chapter - 5) 6.Bayesian Concept Learning : Importance of Bayesian methods, Bayesian theorem, Bayes’ theorem and concept learning, Bayesian Belief Network. (Chapter - 6) 7.Supervised Learning : Classification and Regression : Supervised Learning, Classification Model, Learning steps, Classification algorithms, Regression, Regression algorithms. (Chapter - 7) 8.Unsupervised Learning : Supervised vs. Unsupervised Learning, Applications, Clustering, Association rules (Chapter - 8) 9.Neural Network : Introduction to neural network, Biological and Artificial Neurons, Types of Activation functions, Implementation of ANN, Architecture, Leaning process, Backpropogation, Deep Learning. (Chapter - 9)
