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The ART of Data Mining – Practical learnings from real-world data mining applications

3,100 views · Published 1 September 2014 · 1:18:27 · Indexed 6 November 2026

Channel: Hasgeek TV · 2014 · Science & Technology

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Machine Learning and data mining is part SCIENCE (ML algorithms, optimization), part ENGINEERING (large-scale modelling, real-time decisions), part PROCESS (data understanding, feature engineering, modelling, evaluation, and deployment), and part ART. In this talk, Dr. Shailesh Kumar focuses on the "ART of data mining" - the little things that make the big difference in the quality and sophistication of machine learning models we build. Using real-world analytics problems from a variety of domains, Shailesh shares a number of practical learnings in:

(1) The art of understanding the data better - (e.g. visualization of text data in a semantic space)

(2) The art of feature engineering - (e.g. converting raw inputs into meaningful and discriminative features)

(3) The art of dealing with nuances in class labels - (e.g. creating, sampling, and cleaning up class labels)

(4) The art of combining labeled and unlabelled data - (e.g. semi-supervised and active learning)

(5) The art of decomposing a complex modelling problem into simpler ones - (e.g. divide and conquer)

(6) The art of using textual features with structured features to build models, etc.

The key objective of the talk is to share some of the learnings that might come in handy while "designing" and "debugging" machine learning solutions and to give a fresh perspective on why data mining is still mostly an ART.

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