This book focuses on the Python-based tools and techniques to help you
become highly productive at all aspects of typical data science stacks
such as statistical analysis, visualization, model selection, and
feature engineering.
You'll review the inefficiencies and bottlenecks lurking in the daily
business process and solve them with practical solutions. Automation of
repetitive data science tasks is a key mindset that is promoted
throughout the book. You'll learn how to extend the existing coding
practice to handle larger datasets with high efficiency with the help of
advanced libraries and packages that already exist in the Python
ecosystem.
The book focuses on topics such as how to measure the memory footprint
and execution speed of machine learning models, quality test a data
science pipelines, and modularizing a data science pipeline for app
development. You'll review Python libraries which come in very handy for
automating and speeding up the day-to-day tasks.
In the end, you'll understand and perform data science and machine
learning tasks beyond the traditional methods and utilize the full
spectrum of the Python data science ecosystem to increase productivity.
What You'll Learn
- Write fast and efficient code for data science and machine learning
- Build robust and expressive data science pipelines
- Measure memory and CPU profile for machine learning methods
- Utilize the full potential of GPU for data science tasks
- Handle large and complex data sets efficiently
Who This Book Is For
Data scientists, data analysts, machine learning engineers, Artificial
intelligence practitioners, statisticians who want to take full
advantage of Python ecosystem.
