Machine Learning and Artificial Intelligence are the "Buzz topics" in every trending article of 2021, and rightfully so. It is much like how the internet emerged as a game-changer in everyone's lifestyle, Artificial Intelligence and Machine Learning are poised to transform our lives which were unimaginable years ago. Artificial Intelligence (A.I.) is a simplified problem-solving process for humans. It empowers software to do jobs without being explicitly programmed. Also, it has neural networks and profound learning.

This course model will teach you how to teach, train and create Machine Learning models faster, easier without writing a single line of code. Just come as you are and leave with some understanding of Machine Learning. No prior knowledge is required. Even little children can teach and train a Machine Learning model. There are interesting links to help us understand how AI & ML is/are changing society, the pros and cons and so much more.

This course model will teach you how to teach, train and create Machine Learning models faster, easier without writing a single line of code. Just come as you are and leave with some understanding of Machine Learning. No prior knowledge is required. Even little children can teach and train a Machine Learning model. There are interesting links to help us understand how AI & ML is/are changing society, the pros and cons and so much more.

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on real data from a wide range of domains.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.