Microsoft Summer Internship | Training 2018

Machine Learning


Machine Learning shares a platform where a concise foundation to the fundamental concepts in machine learning and popular machine learning algorithms are used. We will wrapup the standards and most popular supervised learning algorithms including linear regression, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks and many more with an establishment to Deep Learning. We will also wrap up the basic congregate algorithms. FRM (Feature reduction methods) will also be discussed during the training. We will help the students to get the basics of computational learning theory. In ourtraining course there will be a discussion on hypothesis space, overfitting, bias and variance and etc. The course will be conducted by hands-on problem solving exercises with programming in Python and some tutorial sessions with the help of the experienced faculties.


Basic programming skills (in Python), algorithm design, basics of probability & statistics.

About The Course -

Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning.The machine learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

Learning Objectives -

  • Master the concepts of supervised and unsupervised learning.
  • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
  • Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
  • Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
  • Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
  • Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems.

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