Machine Learning and its Applications in Market Research
Overview
13:00-15:00 UTC / 15:00-17:00 CET - Two hours per day over three days.
This course builds on the previous course Python for Market Research. It introduces you to the topic of Machine Learning (ML), how to build predictive models using ML algorithms and to use them for inferencing, decision making and forecasting. Data pre-processing will be an integral part of this course.
The course will focus on learning by doing and so be prepared for hands-on work and exercises. Introductory videos will be made available to help the participant ease into the course.
This course is a follow-up to the prerequisite course:
Python for Market Research. Use code VA_Python25_FDP when buying both for a 25% discount!
This course will cover the following:
Data pre-processing
Machine Learning using Scikit-Learn
Building predictive models
Applications in Market Research and hands-on exercises
After this course, you’ll be able to:
Use different data pre-processing techniques for different kinds of data
Use the ML frameworks in Python like Scikit-Learn for model building
Work with different ML algorithms to build predictive models based on data
Measure and analyze mode accuracy and tweak models for better performance
Discuss applications of ML methods in Market Research
Who should attend?
Anyone who has attended the Python for Market Research course or anyone with intermediate-level programming knowledge in Python and interested in learning Machine Learning methods.
Prerequisites for the course are:
Complete the intro course Python for Market Research OR
Has an intermediate level of Python programming knowledge
High school-level mathematics (Algebra, etc.)
Trainer
Jag Rao
Professor of Practice at University of GeorgiaJagannath Rao runs a complete course on Machine Learning and Data Science as part of Georgia Informatics institute in the engineering school. He retired in 2018 after having done a stint of 33 years at the global conglomerate Siemens and brings rich industry experience in areas ranging from Industrial Automation to Internet of Things. Apart from an education in AI, he has also spent many years in the practical application of these technologies in the business world and that includes the widespread application of "Big Data" technologies in the realm of manufacturing, covering topics such as plant analytics, asset analytics and digital services. In addition to his role at UGA, Jagannath also consults with industry clients in the space of AI & Machine Learning in the context of generating use cases and applying / implementing those technologies.
Jagannath is an Electrical engineer with a graduate degree in Knowledge Engineering (AI) and an MBA. He has worked around the globe in India, Germany, Singapore and the USA. With more than 30 years' experience, including practical hands on implementation, he brings a large body of expertise which he is imparting to the young engineers graduating today and industry professionals.
Jagannath has also served as the Chair of the Advisory Board, the University of Georgia - Athens, College of Engineering, and on the advisory boards of Arizona State University W.P. Carey School of Business, Center for Services Leadership, and the Georgia Institute of Technology, School of Biomedical Engineering, Advisory Board.