Dipanjan (DJ) Sarkar is a Data Science Lead at Applied Materials, leading advanced analytics efforts around computer vision, natural language processing and deep learning. He is also a Google Developer Expert in Machine Learning. He has consulted and worked with several startups as well as Fortune 500 companies like Intel and Open Source organizations like Red Hat \ IBM. He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering.
Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, computer vision and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. Dipanjan is also a published author, having authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing, and Deep Learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://firstname.lastname@example.org and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.
Day 1 - Workshops 28 May
NLP Applications Crash Course
Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying NLP to solve real- world problems. We will leverage machine learning, deep learning and deep transfer learning to solve some popular tasks in NLP including the following:
-Basics of Natural Language and Python for NLP tasks
-Text Processing and Wrangling
-Text Representation – BOW \ Embeddings (a brief)
-Topic Modeling – Case Study on trending topics in research papers from NIPS proceedings
-Text Summarization – Factorization and Deep Transfer Learning Methods
-Text Classification with Machine Learning & Deep Learning \ Deep Transfer Learning
-- ML methods o RNNs\LSTMs o CNNs