Sculpting Data for ML: The first act of Machine Learning

Jigyasa Grover
Jigyasa Grover is a Machine Learning Engineer at Twitter. She has a myriad of experiences from her brief stints at Facebook, National Research Council of Canada, and Institute of Research & Development France involving Data Science, mathematical modeling, and software engineering. Having graduated from the UC San Diego, with a Master's degree in Computer Science, she is presently plying her past experiences & knowledge towards Applied ML in the online advertisements prediction & ranking domain. Red Hat 'Women in Open Source' Academic Award Winner and Google Summer of Code alumna, Jigyasa is an ardent open-source contributor as well. She served as the Director of Women Who Code & Lead of Women Techmakers for a handful of years to help bridge the gender gap in technology. In her quest to build a powerful community of girls and boys alike, and believing in "we rise by lifting others," she mentors aspiring developers and Machine Learning enthusiasts in various global programs.

Rishabh Misra
Rishabh Misra is a Machine Learning Engineer at Twitter. He developed a passion for identifying & tackling novel and practical problems using ML during his research internships at the Indian Institute of Technology Madras, which he further explored during his Master's in Computer Science from the UC San Diego. He combines his past engineering experiences in designing large-scale systems, working at Amazon & Arcesium, and research experiences in Applied Machine Learning to develop distributed Machine Learning relevance systems at Twitter. His explorations have led to several research publications in competitive ML conferences like RecSys, ACL, and WSDM. The ML community has well received the datasets collected (also used in this book) as part of his research. Kaggle recently ranked him as one of the top 20 dataset contributors, & Deeplearning.ai's "Natural Language Processing in TensorFlow" course on Coursera used his Sarcasm Detection dataset for illustrations.