We want to invite you to participate in the ODSC Webinar!
Learn the latest models, advancements, and trends from the top practitioners behind two of the Data Science’s hottest topics: Machine Learning and Deep Learning. During the webinar, you will get to know more about two sessions you can attend at our conference in Bengaluru on August 7-10, 2019.
Date: June 30th, 2019
Time: 11 - 12:00 am IST
To access this webinar, please register using the link below:
A Hands-on Introduction to Natural Language Processing
Speakers: Dipanjan Sarkar (Data Scientist, Red Hat) and Anuj Gupta (Scientist, Intuit)
Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity that 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 the 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 leverage Python 3 and the latest and best state-of-the-art frameworks including NLTK, Gensim, SpaCy, Scikit-Learn, TextBlob, Keras and TensorFlow to showcase our examples. You will be able to learn a fair bit of machine learning as well as deep learning in the context of NLP during this boot camp.
Deep learning powered Genomic Research
Speakers: Dr. C.S.Jyothirmayee (Sr. Scientist, Novozymes South Asia Pvt Ltd), Usha Rengaraju (Principal Data Scientist, Mysuru Consulting Group) and Vijayalakshmi Mahadevan (Faculty Scientist, Institute of Bioinformatics and Applied Biotec)
Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.
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