Python实战：打造现代数据科学家的机器学习实用指南

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"《实用机器学习(英文版)》是一本深入浅出的机器学习指南，由Dipanjan Sarkar、Raghav Bali和Tushar Sharma合著。该书针对现代数据科学家设计，旨在通过实践案例揭示机器学习的各种技术，尤其注重理论与实践相结合，帮助读者理解并掌握这一领域的实际应用。书中涵盖了主要的数据科学语言Python和R，以及新兴且强大的Julia编程语言，这些工具在处理大规模数据集时展现出强大能力。此外，作者还介绍了Spark、Hadoop和Mahout等大数据平台，使读者能够应对复杂的数据处理环境。对于初学者和经验丰富的专业人士来说，这本书都提供了宝贵的学习资源。《实用机器学习》特别关注了当前机器学习领域的前沿进展，如深度学习（Deep learning）和强化学习（Reinforcement learning）。通过实例演示和实用示例，作者消除了高级机器学习方法的神秘感，让读者能够将理论知识转化为实际操作能力。全书结构清晰，以问题解决为导向，适合那些希望通过Python这个广泛使用的工具来构建现实世界智能系统的实践者。书中不仅教授技术，还强调了理解和应用的重要性，确保读者在掌握技术的同时，也能对机器学习的基本原理有深刻理解。《实用机器学习(英文版)》是一本不可或缺的参考书，无论是对于希望入门机器学习的新手，还是寻求提升现有技能的专业人员，它都能提供一个扎实的基础，并引导他们进入机器学习的实际应用领域。"

xvii

About the Authors

Dipanjan Sarkar is a data scientist at Intel, on a mission to make the

world more connected and productive. He primarily works on Data

Science, analytics, business intelligence, application development, and

building large-scale intelligent systems. He holds a master of technology

degree in Information Technology with specializations in Data Science

and Software Engineering from the International Institute of Information

Technology, Bangalore. He is also an avid supporter of self-learning,

especially Massive Open Online Courses and also holds a Data Science

Specialization from Johns Hopkins University on Coursera.

Dipanjan has been an analytics practitioner for several years,

specializing in statistical, predictive, and text analytics. Having a

passion for Data Science and education, he is a Data Science Mentor

at Springboard, helping people up-skill on areas like Data Science and

Machine Learning. Dipanjan has also authored several books on R,

Python, Machine Learning, and analytics, including Text Analytics with

Python, Apress 2016. Besides this, he occasionally reviews technical books

and acts as a course beta tester for Coursera. Dipanjan’s interests include learning about new technology,

financial markets, disruptive start-ups, Data Science, and more recently, artificial intelligence and Deep

Learning.

Raghav Bali is a data scientist at Intel, enabling proactive and data-driven

IT initiatives. He primarily works on Data Science, analytics, business

intelligence, and development of scalable Machine Learning-based

solutions. He has also worked in domains such as ERP and finance with

some of the leading organizations in the world. Raghav has a master’s

degree (gold medalist) in Information Technology from International

Institute of Information Technology, Bangalore.

Raghav is a technology enthusiast who loves reading and playing

around with new gadgets and technologies. He has also authored

several books on R, Machine Learning, and Analytics. He is a shutterbug,

capturing moments when he isn’t busy solving problems.

xxi

Acknowledgments

This book would have definitely not been a reality without the help and support from some excellent people

and organizations that have helped us along this journey. First and foremost, a big thank you to all our

readers for not only reading our books but also supporting us with valuable feedback and insights. Truly,

we have learnt a lot from all of you and still continue to do so. We would like to acknowledge the entire

team at Apress for working tirelessly behind the scenes to create and publish quality content for everyone.

A big shout-out goes to the entire Python developer community, especially to the developers of frameworks

like numpy, scipy, scikit-learn, spacy, nltk, pandas, statsmodels, keras, and tensorflow. Thanks also to

organizations like Anaconda, for making the lives of data scientists easier and for fostering an amazing

ecosystem around Data Science and Machine Learning that has been growing exponentially with time. We

also thank our friends, colleagues, teachers, managers, and well-wishers for supporting us with excellent

challenges, strong motivation, and good thoughts. A special mention goes to Ram Varra for not only being

a great mentor and guide to us, but also teaching us how to leverage Data Science as an effective tool from

technical aspects as well as from the business and domain perspectives for adding real impact and value.

We would also like to express our gratitude to our managers and mentors, both past and present, including

Nagendra Venkatesh, Sanjeev Reddy, Tamoghna Ghosh and Sailaja Parthasarathy.

A lot of the content in this book wouldn’t have been possible without the help from several people and

some excellent resources. We would like to thank Christopher Olah for providing some excellent depictions

and explanation for LSTM models (http://colah.github.io), Edwin Chen for also providing an excellent

depiction for LSTM models in his blog (http://blog.echen.me), Gabriel Moreira for providing some

excellent pointers on feature engineering techniques, Ian London for his resources on the Visual Bag of

Words Model (https://ianlondon.github.io), the folks at DataScience.com, especially Pramit Choudhary,

Ian Swanson, and Aaron Kramer, for helping us cover a lot of ground in model interpretation with skater

(https://www.datascience.com), Karlijn Willems and DataCamp for providing an excellent source of

information pertaining to wine quality analysis (https://www.datacamp.com), Siraj Raval for creating

amazing content especially with regard to time series analysis and recommendation engines, Amar Lalwani

for giving us some vital inputs around time series forecasting with Deep Learning, Harish Narayanan for an

excellent article on neural style transfer (https://harishnarayanan.org/writing), and last but certainly

not the least, François Chollet for creating keras and writing an excellent book on Deep Learning.

I would also like to acknowledge and express my gratitude to my parents, Digbijoy and Sampa, my

partner Durba and my family and well-wishers for their constant love, support, and encouragement that

drive me to strive to achieve more. Special thanks to my fellow colleagues, friends, and co-authors Raghav

and Tushar for slogging many days and nights with me and making this experience worthwhile! Finally, once

again I would like to thank the entire team at Apress, especially Sanchita Mandal, Celestin John, Matthew

Moodie, and our technical reviewer, Jojo Moolayil, for being a part of this wonderful journey.

—Dipanjan Sarkar

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