“Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering” – Prof. Andrew Ng
Feature Engineering is often one of the overlooked aspects of the Data Science lifecycle but is probably one of the most critical steps which can make or break a Data Science project.
In this hands-on live session, we will be working through a sales forecasting problem step-by-step with a key focus on problem identification, data wrangling, feature engineering & EDA, and finally modeling using some structured frameworks and methodologies. Open-source libraries in Python will be used in combination with Google Colab so we spend minimal time in setup and focus on the core session itself.
Pre-requisites: Having a basic knowledge of supervised machine learning, Python and Google Colab
Who is this Webinar for?
Students & Freshers who want to build a career in Data Science
Working professionals who want to transition to a data science career
Data science professionals who want to accelerate their career growth
Lead Data Scientist, SIT Academy. Google Developer Expert - ML
Dipanjan (DJ) Sarkar is a Data Scientist, Published Author, Faculty & Instructor and Consultant in Data Science. He’s Data Science Lead at SIT Academy, AI Advisor & Consultant at Springboard, Data Science Advisor & Consultant at Analytics Vidhya, Machine Learning Engineering Course Instructor & Mentor at University of California, San Diego, and Course Beta Tester at Coursera.
He holds a master of technology degree from IIIT Bangalore, with specializations in Data Science and Software Engineering and completed his post-graduate diploma in Machine Learning and Artificial Intelligence from Columbia University in the City of New York.
He has been recognized as a Google Developer Expert in Machine Learning by Google in 2019. And has been honored with the Top 10 Data Scientists in India, 2020 and 40 under 40 Data Scientists, 2021 awards.
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