Crash Course on Machine Learning and Intro’ to Deep Learning

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This is a crash course, but an in-depth course, which will develop you as a Machine learning specialist. Designed with solutions to real life life problems, this will be a boon for your ongoing projects and the organization you work for. Students and Professors will find the course interesting, hassle free and up-to-date. Surely, the students will be employable Machine Learning Engineers and data scientists. Given by an enthusiastic professor after testing it in classrooms several times. The students can carry out a number of projects using this course.

This exemplary, engaging, enlightening and enjoyable course is organized as seven modules, with abundant worked examples in the form of programs executed on Jupyter Notebook. It is important that data is visualized before attempting to carryout machine learning and hence we start the course with a section on data visualization. This is followed by a full blown exposure to Regression covering simple linear regression, polynomial regression, multiple linear regression and followed by analytic solution through normal equation.  Regression is followed by extensive discussions on another important supervised learning algorithms on Classification. We carry out modeling using classification strategies such as logistic regression, Naive Bayes classifier, support vector machine, K nearest neighbor, Decision trees, ensemble learning, classification and regression trees, random forest and boosting – ada boost, gradient boosting.

From supervised learning we move on to discuss about unsupervised learning – clustering for unlabelled data. We study the hierarchical, k means, k medoids and Agglomerative Clustering. It is not enough to know the algorithms, but also strategies such as bias variance trade off and curse of dimensionality to be successful in this challenging field of current and futuristic importance. We also carry out Principal Component analysis and Linear discriminant analysis to deal with curse of dimensionality.

The last section leads the reader to deep learning through a lucid introduction to Artificial Neural network (ANN) and back propagation algorithm for estimating weights of feed forward network. Before we close, we take up 2 case studies- one on binary classification and another on multi-class classification using ANN, to give a feel of deep learning.

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