Foundations of Machine Learning
Machine learning is one of the fastest growing areas of software engineering, focused on building systems that can learn from historical data, identify patterns, and make logical decisions with little to no human intervention. It is a data analysis method that automates the building of analytical models through using data that encompasses diverse forms of digital information including numbers, words, clicks and images.
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Machine Learning lies at the intersection of the mathematics, programming and the different training algorithms. This course covers the fundamentals in all these areas, so students will gain all the skills necessary to become competent machine learning engineers.
Modules
Mathematical Foundations of Machine Learning
This 5 day course covers the mathematical foundations for Machine Learning. Students are introduced to Applied Math Basics, Linear algebra, Basic Statistics and Probability Theory, Probability Density Functions, Bayesian Rules and single and multi-variable Calculus
Introduction to Python
This 5 day course is an introduction to Programming and students learn the basics of Python so that they can eventually use it for Machine Learning. The course is a building block for the final course in Machine Learning Applications and will introduce learner to basics, using libraries and packages, working with functions and classes.
Machine Learning Algorithms
This 10 day course introduces Machine Learning and builds on the knowledge gained in Mathematics and Programming in the basic courses. Students will learn the different machine learning models such as linear regression, logistic regression, clustering, SVM, Decision Trees as well as fundamentals of Deep Learning including Neural Networks, CNN, RNN, LSTM and applications in Computer Vision and Text Processing.
Who should attend
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Professionals seeking a career in Machine Learning
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Executives from non-software background, looking for a career transition
Prerequisites
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At least a diploma with 2 years of work experience
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Have own laptop or desktop with CPU of at least intel core I3, GPU with an integrated graphics card and RAM of at least 8GB​
Program Delivery and Assessment
The program will be delivered in a hybrid mode, comprising self study, online and face-to-face lessons
The students will have to complete regular assignments and one final assessment at the end of each module