Python data science cookbook : over 60 practical recipes to help you explore Python and its robust data science capabilities

AnnotationOver 60 practical recipes to help you explore Python and its robust data science capabilitiesAbout This Book The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python Get up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipesWho This Book Is ForThis book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience. What You Will Learn Explore the complete range of Data Science algorithms Get to know the tricks used by industry engineers to create the most accurate data science models Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively Create meaningful features to solve real-world problems Take a look at Advanced Regression methods for model building and variable selection Get a thorough understanding of the underlying concepts and implementation of Ensemble methods Solve real-world problems using a variety of different datasets from numerical and text data modalities Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so onIn DetailPython is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It's a disruptive technology changing the face of today's business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way. This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly. The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional. Style and approachThis is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms. Read more... Abstract: Annotation Over 60 practical recipes to help you explore Python and its robust data science capabilitiesAbout This Book The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python Get up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipesWho This Book Is ForThis book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience. What You Will Learn Explore the complete range of Data Science algorithms Get to know the tricks used by industry engineers to create the most accurate data science models Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively Create meaningful features to solve real-world problems Take a look at Advanced Regression methods for model building and variable selection Get a thorough understanding of the underlying concepts and implementation of Ensemble methods Solve real-world problems using a variety of different datasets from numerical and text data modalities Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so onIn DetailPython is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It's a disruptive technology changing the face of today's business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way. This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly. The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional. Style and approachThis is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms

Over 60 practical recipes to help you explore Python and its robust data science capabilities

About This Book

• The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action
• Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python
• Get up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipes

Who This Book Is For

This book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience.

What You Will Learn

• Explore the complete range of Data Science algorithms
• Get to know the tricks used by industry engineers to create the most accurate data science models
• Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively
• Create meaningful features to solve real-world problems
• Take a look at Advanced Regression methods for model building and variable selection
• Get a thorough understanding of the underlying concepts and implementation of Ensemble methods
• Solve real-world problems using a variety of different datasets from numerical and text data modalities
• Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so on

In Detail

Python is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It's a disruptive technology changing the face of today's business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way.

This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly.

The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional.

Style and approach

This is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms.

Over 60 practical recipes to help you explore Python and its robust data science capabilities About This Book • The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action • Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python • Get up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipes Who This Book Is For This book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience. What You Will Learn • Explore the complete range of Data Science algorithms • Get to know the tricks used by industry engineers to create the most accurate data science models • Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively • Create meaningful features to solve real-world problems • Take a look at Advanced Regression methods for model building and variable selection • Get a thorough understanding of the underlying concepts and implementation of Ensemble methods • Solve real-world problems using a variety of different datasets from numerical and text data modalities • Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so on In Detail Python is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It’s a disruptive technology changing the face of today’s business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way. This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly. The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional. Style and approach This is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms. About This BookThe book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in actionExplore concepts such as programming, data mining, data analysis, data visualization, and machine learning using PythonGet up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipesWho This Book Is ForThis book is intended for all levels of data science professionals, both students and practitioners from novice to experts. Different recipes in the chapters cater to the needs of different audiences. Novice readers can spend some time in getting themselves acquainted with data science in the first five chapters. Experts can refer to the later chapters to refer/understand how advanced techniques are implemented using Python. The book covers just enough mathematics and provides the necessary references for computer programmers who wish to understand data science. People from a non-Python background can effectively use this book. The first chapter of the book introduces Python as a programming language for data science. It will be helpful if you have some prior basic programming experience. The book is mostly self-contained and introduces data science to a new reader and can help him become an expert in this trade.What You Will LearnExplore the complete range of data science algorithmsManage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectivelyTake a look at advanced regression methods for model building and variable selectionDevelop a thorough understanding of the underlying concepts and implementation of ensemble methodsSolve real-world problems using a variety of different datasets from numerical and text data modalitiesBecome accustomed to modern state-of-the art algorithms such as gradient boosting, random forest, rotation forest, and moreIn DetailPython is increasingly becoming the language for data science.This book will walk you through the various concepts, starting from simple algorithms, to the most complex available in the data science arsenal, to effectively mine data and derive intelligence from it.The book begins by introducing you to the use of Python for data science, followed by how to work with Python environments. You will then learn how to analyze your data with Python. The book then teaches you about the concept of data mining, followed by extensive coverage of machine learning methods. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are must-haves for any successful data science professional. During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics.Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit. FROM THE REVIEWS: TECHNOMETRICS "[This] is a vast and complex book. Generally, it concentrates on explaining why and how the methods work, rather than how to use them. Examples and especially the visualizations are principle features...As a source for the methods of statistical learning...it will probably be a long time before there is a competitor to this book."

This is author-approved bcc: Multivariate statistical methods have evolved from the pioneering work of Fisher, Pearson, Hotelling,and others, motivated by practical problems in biological and other sciences. In the past fifty years the field has grown rapidly, largely due to the availability of computers that make the calculations feasible. This book gives a comprehensive and self-contained introduction, carefully balancing mathematical theory and practical applications. A First Course in Multivariate Statistics starts at an elementary level, developing concepts of multivariate distributions from first principles. A chapter on the multivariate normal distribution reviews the classical parametric theory. Methods of estimation are explored using the plug-in principles as well as maximum likelihood. Two chapters on discrimination and classification, including logistic regression, are at the core of the book. Methods of testing hypotheses are developed from heuristic principles, followed by likelihood ratio tests and permutation tests. The powerful self- consistency principle is used to introduce principal components as a method of approximation. The book concludes with a chapter on finite mixture analysis, a topic of great practical and theoretical importance. Unique features of A First Course in Multivariate Statistics include the presentation of the EM algorithm for maximum likelihood estimation with incomplete data, resampling based methods of testing, a brief introduction to the theory of elliptical distributions, and a comparison of linear and quadratic classification rules. Examples from biology, anthropology, chemistry, and other area are worked out

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boostingthe first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

Neural Networks and Learning Machines

Third Edition

Simon Haykin

McMaster University, Canada

This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include:

• On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scale learning problems.

• Kernel methods, including support vector machines, and the representer theorem.

• Information-theoretic learning models, including copulas, independent components analysis (ICA), coherent ICA, and information bottleneck.

• Stochastic dynamic programming, including approximate and neurodynamic procedures.

• Sequential state-estimation algorithms, including Kalman and particle filters.

• Recurrent neural networks trained using sequential-state estimation algorithms.

• Insightful computer-oriented experiments.

Just as importantly, the book is written in a readable style that is Simon Haykin’s hallmark.

This book presents the first comprehensive treatment of neural networks from an engineering perspective. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology.

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide''data (p bigger than n), including multiple testing and false discovery rates. My goal in writing this book has been to provide teachers and students of multi­ variate statistics with a unified treatment ofboth theoretical and practical aspects of this fascinating area. The text is designed for a broad readership, including advanced undergraduate students and graduate students in statistics, graduate students in bi­ ology, anthropology, life sciences, and other areas, and postgraduate students. The style of this book reflects my beliefthat the common distinction between multivariate statistical theory and multivariate methods is artificial and should be abandoned. I hope that readers who are mostly interested in practical applications will find the theory accessible and interesting. Similarly I hope to show to more mathematically interested students that multivariate statistical modelling is much more than applying formulas to data sets. The text covers mostly parametric models, but gives brief introductions to computer-intensive methods such as the bootstrap and randomization tests as well. The selection of material reflects my own preferences and views. My principle in writing this text has been to restrict the presentation to relatively few topics, but cover these in detail. This should allow the student to study an area deeply enough to feel comfortable with it, and to start reading more advanced books or articles on the same topic. "Multivariate statistical methods have evolved from the pioneering work of Fisher, Pearson, Hotelling, and others, motivated by practical problems in biological and other sciences. In the past fifty years, the field has grown rapidly, largely due to the availability of computers that make the calculations feasible. This book gives a comprehensive and self-contained introduction, carefully balancing mathematical theory and practical applications." "Unique features of A First Course in Multivariate Statistics include the presentation of the EM algorithm for maximum likelihood estimation with incomplete data, resampling-based methods of testing, a brief introduction to the theory of elliptical distributions, and a comparison of linear and quadratic classification rules. Examples from biology, anthropology, chemistry, and other areas are worked out in detail." "The book contains a wealth of exercises, ranging from easy to advanced, making it an ideal text for the classroom. Many graphical illustrations help the student develop an intuitive understanding of the mathematical concepts."--Jacket For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently. A comprehensive and self-contained introduction to the field, carefully balancing mathematical theory and practical applications. It starts at an elementary level, developing concepts of multivariate distributions from first principles. After a chapter on the multivariate normal distribution reviewing the classical parametric theory, methods of estimation are explored using the plug-in principles as well as maximum likelihood. Two chapters on discrimination and classification, including logistic regression, form the core of the book, followed by methods of testing hypotheses developed from heuristic principles, likelihood ratio tests and permutation tests. Finally, the powerful self-consistency principle is used to introduce principal components as a method of approximation, rounded off by a chapter on finite mixture analysis.