Sculpting Data for ML: The first act of Machine Learning


In the contemporary world of machine learning algorithms - “data is the new oil”. For the state-of-the-art ML algorithms to work their magic it’s important to lay a strong foundation with access to relevant data. Volumes of crude data are available on the web nowadays, and all we need are the skills to identify and extract meaningful datasets. This talk aims to present the power of the most fundamental aspect of Machine Learning - Dataset Curation, which often does not get its due limelight. It will also walk the audience through the process of constructing good quality datasets as done in formal settings with a simple hands-on Pythonic example (based on audience/format of session). The goal is to institute the importance of data, especially in its worthy format, and the spell it casts on fabricating smart learning algorithms.

Session Outline

Introduction (10 minutes)
Popularity of Machine Learning & Applications
Significance of honing dataset building skills
Importance in Academia: Expanding domains to perform research on, Solving novel problems using ML, Leading research efforts in this domain, etc.
Importance in Industry: Availability of lots of raw data, no exact dataset available for training purposes, Proactively identifying data to log to solve specific problems, etc.
Finding data source(s) (10 minutes)
Guided Search based on a problem definition: Identifying essential data signals
Unguided Search with no problem definition in mind: Dealing with ambiguity
Tips on identifying data sources.
Data Extraction - Hands-On Example (Audience-level & Time-constraint dependent) (30 - 45 minutes)
Live Python example implemented via Jupyter Notebook
Use of Python tools: BeautifulSoup and Selenium
Step-by-step process to plan data extraction
Nitty-gritty details about tools and the extraction code itself


Jigyasa Grover is a Machine Learning Engineer at Twitter, ML Google Developer Expert and the co-author of the book ‘Sculpting Data for ML’. She has a myriad of experiences from her brief stints at Facebook, Inc., National Research Council of Canada, and Institute of Research & Development France involving Data Science, mathematical modeling, and software engineering. Having graduated from the University of California, San Diego, with a Master’s degree in Computer Science with an Artificial Intelligence specialization, she is presently plying her past experiences and knowledge towards Applied Machine Learning in the online advertisements prediction and ranking domain.

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