Resource Person on Machine Learning

This is an abstract of an event where I was invited as Resource Person on workshop on Machine Learning held at Maharaja Institute of Technology, Thandavapura (Mysore).

Pravin Hanchinal explaining AI

What is Artificial Intelligence?

Artificial Intelligence is the science of intelligent programs based on data.

As they say, Artificial Intelligence (AI) the Last Invention We Will Ever Make. But to have a working artificially intelligent machines, the machine needs to learn as we do.

What is Machine Learning?

To make machines learn, the traditional programming paradigm won’t work efficiently. Hence, we need a new way of making machines learn. This new approach to programming is called “Machine Learning”.

Machine Learning is a scientific study of algorithms and statistical models that make systems to automatically learn and improve from experience without being explicitly programmed.

To make ‘Machines’ learn without being programmed explicitly, we need algorithms, mathematical and statistical concepts. Some of them are as mentioned below:

  • Linear algebra
  • Probability theory-Probability and Statistics
  • Calculus
  • Graph theory
  • Optimization methods
Praveen Hanchinal explaining TensorFlow (machine learning)


In my sessions at the venue, the talk was focused on hands-on sessions for final year students. First students were taught Machine Learning basics using TensorFlow and other tools.

Hello World in Machine Learning

The first session was involved in training a model to perform a prediction(Hello World) in Google Colabs using python programming language. In this session, students learnt the importance of the following concepts:

  • Why Keras is used?
  • How to define layers?
  • What is optimizer?
  • How errors are calculated?
  • Why define Epochs?
  • Why use numpy?
MIT students working on CoLabs

The next session involved discussions on various supervised and unsupervised learning models.

  • Bayesian network considering medical data.
  • Expectation–maximization(EM) algorithm vs K means using the iris data set
  • naïve Bayesian classifier
  • ANN Backpropagation

On a final note, the importance of training models using GPU over CPU was discussed. Added to previous discussions various public and open datasets were explored. Thanks to N N Naveen for his company throughout the day.

Pravin Hanchinal

A professionally qualified and highly skilled, experienced Educator, Professional Speaker on Cloud, Big Data, IoT (Internet of Things) and AI (Artificial Intelligence). Have been working on Cloud, Big Data, IoT technologies for last 5.5 years. With his extensive knowledge and skills, has worked as Team Lead, Asst. Professor and Trainer. Trained around 10000+ people which include teachers, students, corporate and government audience on recent technologies. He is known for his great presentation skills and sense of humor. His analogical explanations make the audience to gain the sturdy concepts in the simplest way. He is available for the keynote address, seminars, conducting a workshop for students, faculty, industry and general audiences. In Nutshell: Edupreneur, FOSS, Cloud, Big Data, IoT and AI Evangelist

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2 Responses

  1. Dr. P. Abdul Khayum says:

    Respected Sir,
    Are you willing to come to our college as a resorce person for ONe Week STTP program.
    Please give your Mobile number.

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