Python Machine Learning By Example Second Edition

Implement machine learning algorithms and techniques to build intelligent systems

Yuxi (Hayden) Liu 지음

Packt(GCO Science) 2019년 02월 28일 출간

작품소개

책소개

▶What You Will Learn
？ Understand the important concepts in machine learning and data science
？ Use Python to explore the world of data mining and analytics
？ Scale up model training using varied data complexities with Apache Spark
？ Delve deep into text and NLP using Python libraries such NLTK and gensim
？ Select and build an ML model and evaluate and optimize its performance
？ Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn

▶Key Features
？ Exploit the power of Python to explore the world of data mining and data analytics
？ Discover machine learning algorithms to solve complex challenges faced by data scientists today
？ Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects

▶Who This Book Is For
If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.

목차

▶TABLE of CONTENTS
1 Getting Started with Machine Learning and Python
2 Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
3 Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
4 Detecting Spam Email with Naive Bayes
5 Classifying Newsgroup Topics with Support Vector Machines
6 Predicting Online Ad Click-Through with Tree-Based Algorithms
7 Predicting Online Ad Click-Through with Logistic Regression
8 Scaling Up Prediction to Terabyte Click Logs
9 Stock Price Prediction with Regression Algorithms
10 Machine Learning Best Practices

출판사 서평

▶ Preface
The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.

Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.

With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.

By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.

작가정보

저자(글) Yuxi (Hayden) Liu

구글의 머신러닝 소프트웨어 엔지니어. 구글 이전에는 여러 데이터 기반 도메인에서 머신러닝 과학자로 일하며 디지털 광고, 마케팅, 사이버 보안에 관한 전문 지식을 발휘했다. 현재 세계에서 가장 큰 검색 엔진에서 광고 최적화를 위한 머신러닝 모델과 시스템을 개발하고 개선하는 일을 한다. 교육에 대한 열정이 커 여러 머신러닝 도서를 집필했다. 첫 책인 《Python Machine Learning By Example》의 초판은 2017년과 2018년에 아마존에서 베스트셀러 1위에 올랐으며 다양한 언어로 번역되었다. 집필한 책으로는 《Machine Learning with PyTorch and Scikit-Learn》(2022), 《Deep Learning with R for Beginners》(2019), 《Hands-On Deep Learning Architectures with Python》(2019), 《R Deep Learning Projects》(이상 Packt, 2018)가 있다.