Название: Sculpting Data for ML: The first act of Machine Learning
Автор: Jigyasa Grover, Rishabh Misra, Mengting Wan
Издательство: Jigyasa Grover & Rishabh Misra
Год: 2021
Страниц: 189
Язык: английский
Формат: pdf, azw3, epub
Размер: 17.8 MB

In the contemporary world of Artificial Intelligence and Machine Learning, data is the new oil. For Machine Learning algorithms to work their magic, it is imperative to lay a firm foundation with relevant data. Sculpting Data for ML introduces the readers to the first act of Machine Learning, Dataset Curation. This book puts forward practical tips to identify valuable information from the extensive amount of crude data available at our fingertips. The step-by-step guide accompanies code examples in Python from the extraction of real-world datasets and illustrates ways to hone the skills of extracting meaningful datasets. In addition, the book also dives deep into how data fits into the Machine Learning ecosystem and tries to highlight the impact good quality data can have on the Machine Learning system's performance.

Many recent breakthroughs in Machine Learning, including Natural Language Processing, Computer Vision, etc. owe as much to having better data as they owe to having better models. Naturally, modern ML datasets should be large , in order for models to capture their complex underlying semantics. However having enough data is only a small part of the problem: data must also be processed, appropriately represented, properly sampled, freed from issues of balance and bias etc., not to mention the challenge of extracting meaningful predictive information.

What's Inside?
Significance of data in Machine Learning
Identification of relevant data signals
End-to-end process of data collection and dataset construction
Overview of extraction tools like BeautifulSoup and Selenium
Step-by-step guide with Python code examples of real-world use cases
Synopsis of Data Preprocessing and Feature Engineering techniques
Introduction to Machine Learning paradigms from a data perspective

This book is for Machine Learning researchers, practitioners, or enthusiasts who want to tackle the data availability challenges to address real-world problems.