Python: Deeper Insights into Machine Learning

Leverage benefits of machine learning techniques using Python

Sebastian Raschka , David Julian , John Hearty 지음

Packt(GCO Science) 2016년 08월 31일 출간

작품소개

책소개

▶Book Description
Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.

The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems.
The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems.
At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.

▶About This Book
? Improve and optimise machine learning systems using effective strategies.
? Develop a strategy to deal with a large amount of data.
? Use of Python code for implementing a range of machine learning algorithms and techniques.

▶What You Will Learn
? Learn to write clean and elegant Python code that will optimize the strength of your algorithms
? Uncover hidden patterns and structures in data with clustering
? Improve accuracy and consistency of results using powerful feature engineering techniques
? Gain practical and theoretical understanding of cutting-edge deep learning algorithms
? Solve unique tasks by building models
? Get grips on the machine learning design process

▶Who This Book Is For
This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts.

▶Style and approach
This course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries. This course will explain important machine learning models in a step-by-step manner. Each topic is well explained with real-world applications with detailed guidance.Through this comprehensive guide, you will be able to explore machine learning techniques.

▶What this book covers
? Module 1, Python Machine Learning, discusses the essential machine algorithms for classification and provides practical examples using scikit-learn. It teaches you to prepare variables of different types and also speaks about polynomial regression and tree-based approaches. This module focuses on open source Python library that allows us to utilize multiple cores of modern GPUs.
? Module 2, Designing Machine Learning Systems with Python, acquaints you with large library of packages for machine learning tasks. It introduces broad topics such as big data, data properties, data sources, and data processing .You will further explore models that form the foundation of many advanced nonlinear techniques. This module will help you in understanding model selection and parameter tuning techniques that could help in various case studies.
? Module 3, Advanced Machine Learning with Python, helps you to build your skill with deep architectures by using stacked denoising autoencoders. This module is a blend of semi-supervised learning techniques, RBM and D

목차

▶TABLE of CONTENTS
1: GIVING COMPUTERS THE ABILITY TO LEARN FROM DATA
2: TRAINING MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION
3: A TOUR OF MACHINE LEARNING CLASSIFIERS USING SCIKIT-LEARN
4: BUILDING GOOD TRAINING SETS ？ DATA PREPROCESSING
5: COMPRESSING DATA VIA DIMENSIONALITY REDUCTION
6: LEARNING BEST PRACTICES FOR MODEL EVALUATION AND HYPERPARAMETER TUNING
7: COMBINING DIFFERENT MODELS FOR ENSEMBLE LEARNING
8: APPLYING MACHINE LEARNING TO SENTIMENT ANALYSIS
9: EMBEDDING A MACHINE LEARNING MODEL INTO A WEB APPLICATION
10: PREDICTING CONTINUOUS TARGET VARIABLES WITH REGRESSION ANALYSIS
11: WORKING WITH UNLABELED DATA ？ CLUSTERING ANALYSIS
12: TRAINING ARTIFICIAL NEURAL NETWORKS FOR IMAGE RECOGNITION
13: PARALLELIZING NEURAL NETWORK TRAINING WITH THEANO
14: THINKING IN MACHINE LEARNING
15: TOOLS AND TECHNIQUES
16: TURNING DATA INTO INFORMATION
17: MODELS ？ LEARNING FROM INFORMATION
18: LINEAR MODELS
19: NEURAL NETWORKS
20: FEATURES ？ HOW ALGORITHMS SEE THE WORLD
21: LEARNING WITH ENSEMBLES
22: DESIGN STRATEGIES AND CASE STUDIES
23: UNSUPERVISED MACHINE LEARNING
24: DEEP BELIEF NETWORKS
25: STACKED DENOISING AUTOENCODERS
26: CONVOLUTIONAL NEURAL NETWORKS
27: SEMI-SUPERVISED LEARNING
28: TEXT FEATURE ENGINEERING
29: FEATURE ENGINEERING PART II
30: ENSEMBLE METHODS
31: ADDITIONAL PYTHON MACHINE LEARNING TOOLS
32: CHAPTER CODE REQUIREMENTS

책 속으로

▶What this book covers
？ Module 1, Python Machine Learning, discusses the essential machine algorithms for classification and provides practical examples using scikit-learn. It teaches you to prepare variables of different types and also speaks about polynomial regression
and tree-based approaches. This module focuses on open source Python library that allows us to utilize multiple cores of modern GPUs.
？ Module 2, Designing Machine Learning Systems with Python, acquaints you with large library of packages for machine learning tasks. It introduces broad topics such
as big data, data properties, data sources, and data processing .You will further
explore models that form the foundation of many advanced nonlinear techniques.
This module will help you in understanding model selection and parameter tuning
techniques that could help in various case studies.
？ Module 3, Advanced Machine Learning with Python, helps you to build your skill
with deep architectures by using stacked denoising autoencoders. This module is a
blend of semi-supervised learning techniques, RBM and DBN algorithms .Further
this focuses on tools and techniques which will help in making consistent working
process.

출판사 서평

▶Editorial Review
Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace .It is one of the fastest growing trends in modern computing and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a Learning Path that will help you to tackle the realworld complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems

작가정보

저자(글) Sebastian Raschka

？ Sebastian RaschkaSebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python.While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background.His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle

저자(글) David Julian

？ David Julian David Julian is currently working on a machine learning project with Urban Ecological Systems Ltd and Blue Smart Farms ( http://www.bluesmartfarms.com.au) to detect and predict insect infestation in greenhouse crops. Dave is a technology consultant, trainer, and musician. Dave has over 15 years' experience as a programmer, web developer, and in teaching small groups. He is proficient in HTML/CSS/JavaScript and PHP and is also an Python enthusiast. Dave is currently investigating data science applications using the Python programming language and relevant machine learning and data science packages. Dave has built virtual private networks and mail servers and managed Windows networks. He has authored a book for us titled Designing Machine Learning Systems with Python and has also been a Technical Reviewer for one of our books, Python Machine Learning.

저자(글) John Hearty

？John HeartyJohn Hearty is a consultant in digital industries with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics.Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences.Eventually John struck out on his own as a consultant offering comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favourite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network.After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfil a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation. He currently lives in the UK.