Practical Machine Learning - Part 2 and Quantum Computing

Practical Machine Learning - Part 2
Speaker: Bharati Patidar https://www.linkedin.com/in/bharati-patidar/
Practical Machine Learning will be a three part series where we deconstruct machine learning. With this series, we will cover the basics of data science and machine learning. The sessions would be technical with a deep dive to important algorithms. The session would also cover some basic mathematics needed to get started into machine learning. We would also talk about few industrial use cases we have worked upon in ML domain. Prerequisite to these sessions would be some experience in any programming language. Sessions would mainly be conducted using Python and Jupyter Notebooks.

Agenda

1. Deep dive into Gradient Descent Algorithm.
2. NLP Basics and Word Embeddings.
3. Language Modeling.
4. Discussion on NLP use cases.

From Uncertainty to Infinity using Quantum Computing
Speaker: Trupti Phadnis https://www.linkedin.com/in/trupti-phadnis-3a837418/
The aim of the talk will be to apprise the audience with the basic concepts of quantum computing, its developmental history and its applications that will shape the future state of computing. Although this field requires knowledge of a large corpus of advanced mathematics and physics, I will present the basic ideas of quantum computing understandable to anyone who has fundamental understanding of computing, algorithms and some Newtonian Physics.

Agenda

1. The Changing Computing Landscapes
2. History and Principles of Quantum Mechanics
3. Quantum Computing, its Application and Challenges
4. Current and Future State