- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
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.
Contents
Chapter 1: What is Productive and Efficient Data Science.- Chapter 2: Better Programming Principles for Efficient Data Science.- Chapter 3: How to Use Python Data Science Packages more Productively.- Chapter 4: Writing Machine Learning Code More Productively.- Chapter 5: Modular and Productive Deep Learning Code.- Chapter 6: Build Your Own Machine Learning Estimator/Package.- Chapter 7: Some Cool Utility Packages.- Chapter 8: Testing the Machine Learning Code.- Chapter 9: Memory and Timing Profiling.- Chapter 10: Scalable Data Science.- Chapter 11: Parallelized Data Science.- Chapter 12: GPU-Based Data Science for High Productivity.- Chapter 13: Other Useful Skills to Master.- Chapter 14: Wrapping It Up.
