Machine Learning Algorithms Second Edition

Popular algorithms for data science and machine learning

Giuseppe Bonaccorso 지음

Packt(GCO Science) 2018년 08월 30일 출간

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목차

▶TABLE of CONTENTS
1: A GENTLE INTRODUCTION TO MACHINE LEARNING
2: IMPORTANT ELEMENTS IN MACHINE LEARNING
3: FEATURE SELECTION AND FEATURE ENGINEERING
4: REGRESSION ALGORITHMS
5: LINEAR CLASSIFICATION ALGORITHMS
6: NAIVE BAYES AND DISCRIMINANT ANALYSIS
7: SUPPORT VECTOR MACHINES
8: DECISION TREES AND ENSEMBLE LEARNING
9: CLUSTERING FUNDAMENTALS
10: ADVANCED CLUSTERING
11: HIERARCHICAL CLUSTERING
12: INTRODUCING RECOMMENDATION SYSTEMS
13: INTRODUCING NATURAL LANGUAGE PROCESSING
14: TOPIC MODELING AND SENTIMENT ANALYSIS IN NLP
15: INTRODUCING NEURAL NETWORKS
16: ADVANCED DEEP LEARNING MODELS
17: CREATING A MACHINE LEARNING ARCHITECTURE

출판사 서평

▶ Preface
This book is an introduction to the world of machine learning, a topic that is becoming more and more important, not only for IT professionals and analysts but also for all the data scientists and engineers who want to exploit the enormous power of techniques such as predictive analysis, classification, clustering, and natural language processing. In order to facilitate the learning process, all theoretical elements are followed by concrete examples based on Python.

A basic but solid understanding of this topic requires a foundation in mathematics, which is not only necessary to explain the algorithms, but also to let the reader understand how it's possible to tune up the hyperparameters in order to attain the best possible accuracy. Of course, it's impossible to cover all the details with the appropriate precision. For this reason, some topics are only briefly described, limiting the theory to the results without providing any of the workings. In this way, the user has the double opportunity to focus on the fundamental concepts (without too many mathematical complications) and, through the references, examine in depth all the elements that generate interest.

The chapters can be read in no particular order, skipping the topics that you already know. Whenever necessary, there are references to the chapters where some concepts are explained. I apologize in advance for any imprecision, typos or mistakes, and I'd like to thank all the Packt editors for their collaboration and constant attention.

인물정보

저자(글) Giuseppe Bonaccorso

12년 이상의 경력을 겸비한 머신 러닝 및 빅데이터 컨설턴트다. 이탈리아 카타니아 대학(University of Catania) 전자 공학과에서 학위를 받았다. 이후 이탈리아 토르 베르가타(Tor Vergata)의 로마 대학 및 영국 에식스(Essex) 대학에서 대학원 전문 과정을 이수했다. 공공 행정, 군대, 공공 시설, 의료, 진단 및 광고 등 다양한 비즈니스 환경에서 IT 경력을 쌓았다. 또한 자바(Java), 파이썬(Python), 하둡(Hadoop), 스파크(Spark), 테아노(Theano) 및 텐서플로(TensorFlow)를 비롯한 많은 기술을 사용해 프로젝트를 수행하고 관리한 경험이 있다. 주요 관심사는 인공 지능, 머신 러닝, 데이터 과학 및 철학 분야다.