- Duration: 3 days
The aim of this course is to provide a basic proficiency in applying Machine Learning methods and artificial Intelligence in practice. The three-day program aims to develop competence and skill in AI and ML for participants. With case studies and demos, the participant will develop a practitioner’s understanding of applicability, limitations and suitability of various techniques to a variety of problems they are likely to face in their work environments. It will involve demonstrations and implementation on cloud platforms or local systems using Python. The program gives a firm foundation in the domain by covering the most popular and widely used AI & ML technologies and applications including Machine Learning, Deep Learning, Neural Network, and Artificial Intelligence.
The objective of the course is to provide participants with a basic concept of machine learning technology related to artificial intelligence. Participants would understand the basic theory of machine learning, classifiers, neural networks, and artificial intelligence and gain a firm exposure to popular AI & ML technologies. Participants would develop basic capability to develop ability to choose the appropriate ML model to independently solve problems using AI & ML. Participants would also understand how popular AI & ML technologies like Python can be used to develop applications
Foundations for AI/ML
The AI & ML foundation course is perfect for beginners who want to explore and take a step forward in this domain.
o Unsupervised Learning
Clustering (k-means, hierarchical, high-dimensional)
meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).
This is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
Neural Network Basics
is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.
Limitations and Challenges to AI/ML 7. Future scope / Research Areas