• Computers

Fundamentals of Machine Learning for Predictive Data Analytics

Algorithms, Worked Examples, and Case Studies
Author: John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher: MIT Press
ISBN: 0262029448
Category: Computers
Page: 624
View: 2537
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

    • Computers

Fundamentals of Machine Learning for Predictive Data Analytics

Algorithms, Worked Examples, and Case Studies
Author: John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher: MIT Press
ISBN: 0262331748
Category: Computers
Page: 624
View: 6193
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

    • Computers

Machine Learning

The Art and Science of Algorithms that Make Sense of Data
Author: Peter Flach
Publisher: Cambridge University Press
ISBN: 1107096391
Category: Computers
Page: 396
View: 4733
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

    • Computers

Statistical and Machine-Learning Data Mining:

Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition
Author: Bruce Ratner
Publisher: CRC Press
ISBN: 1351652389
Category: Computers
Page: 662
View: 8270
The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. is a compilation of new and creative data mining techniques, which address the scaling-up of the framework of classical and modern statistical methodology, for predictive modeling and analysis of big data. SM-DM provides proper solutions to common problems facing the newly minted data scientist in the data mining discipline. Its presentation focuses on the needs of the data scientists (commonly known as statisticians, data miners and data analysts), delivering practical yet powerful, simple yet insightful quantitative techniques, most of which use the "old" statistical methodologies improved upon by the new machine learning influence.

    • Computers

Applied Predictive Analytics

Principles and Techniques for the Professional Data Analyst
Author: Dean Abbott
Publisher: John Wiley & Sons
ISBN: 111872769X
Category: Computers
Page: 456
View: 4414
Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

    • Computers

Fundamentals of Predictive Text Mining


Author: Sholom M. Weiss,Nitin Indurkhya,Tong Zhang
Publisher: Springer
ISBN: 1447167503
Category: Computers
Page: 239
View: 3107
This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.

    • Computers

Understanding Machine Learning

From Theory to Algorithms
Author: Shai Shalev-Shwartz,Shai Ben-David
Publisher: Cambridge University Press
ISBN: 1107057132
Category: Computers
Page: 409
View: 2749
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

    • Computers

Python Machine Learning Case Studies

Five Case Studies for the Data Scientist
Author: Danish Haroon
Publisher: Apress
ISBN: 1484228235
Category: Computers
Page: 204
View: 3350
Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources. Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs. By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems. What You Will Learn Gain insights into machine learning concepts Work on real-world applications of machine learning Learn concepts of model selection and optimization Get a hands-on overview of Python from a machine learning point of view Who This Book Is For Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.

    • Computers

Machine Learning

An Algorithmic Perspective, Second Edition
Author: Stephen Marsland
Publisher: CRC Press
ISBN: 1498759785
Category: Computers
Page: 457
View: 5144
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.

    • Computers

Evaluating Learning Algorithms

A Classification Perspective
Author: Nathalie Japkowicz,Mohak Shah
Publisher: Cambridge University Press
ISBN: 1139494147
Category: Computers
Page: N.A
View: 1213
The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.

    • Computers

Practical Machine Learning Cookbook


Author: Atul Tripathi
Publisher: Packt Publishing Ltd
ISBN: 1785286536
Category: Computers
Page: 570
View: 3284
Resolving and offering solutions to your machine learning problems with R About This Book Implement a wide range of algorithms and techniques for tackling complex data Improve predictions and recommendations to have better levels of accuracy Optimize performance of your machine-learning systems Who This Book Is For This book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected. What You Will Learn Get equipped with a deeper understanding of how to apply machine-learning techniques Implement each of the advanced machine-learning techniques Solve real-life problems that are encountered in order to make your applications produce improved results Gain hands-on experience in problem solving for your machine-learning systems Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model's performance, and improving the model's performance In Detail Machine learning has become the new black. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you'll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one. Style and approach Following a cookbook approach, we'll teach you how to solve everyday difficulties and struggles you encounter.

    • Business & Economics

Data Science in R

A Case Studies Approach to Computational Reasoning and Problem Solving
Author: Deborah Nolan,Duncan Temple Lang
Publisher: CRC Press
ISBN: 1482234823
Category: Business & Economics
Page: 539
View: 7831
Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book’s collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data. Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers’ computational reasoning of real-world data analyses.

    • Business & Economics

Predictive Analytics For Dummies


Author: Dr. Anasse Bari,Mohamed Chaouchi,Tommy Jung
Publisher: John Wiley & Sons
ISBN: 1119267013
Category: Business & Economics
Page: 456
View: 8601
Use Big Data and technology to uncover real-world insights You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in. Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away! Offers common use cases to help you get started Covers details on modeling, k-means clustering, and more Includes information on structuring your data Provides tips on outlining business goals and approaches The future starts today with the help of Predictive Analytics For Dummies.

    • Computers

Data Science and Big Data Analytics

Discovering, Analyzing, Visualizing and Presenting Data
Author: EMC Education Services
Publisher: John Wiley & Sons
ISBN: 1118876059
Category: Computers
Page: 432
View: 693
Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available at www.wiley.com/go/9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

    • Computers

Spark for Data Science


Author: Srinivas Duvvuri,Bikramaditya Singhal
Publisher: Packt Publishing Ltd
ISBN: 1785884778
Category: Computers
Page: 344
View: 5854
Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0 About This Book Perform data analysis and build predictive models on huge datasets that leverage Apache Spark Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges Work through practical examples on real-world problems with sample code snippets Who This Book Is For This book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you! What You Will Learn Consolidate, clean, and transform your data acquired from various data sources Perform statistical analysis of data to find hidden insights Explore graphical techniques to see what your data looks like Use machine learning techniques to build predictive models Build scalable data products and solutions Start programming using the RDD, DataFrame and Dataset APIs Become an expert by improving your data analytical skills In Detail This is the era of Big Data. The words ҂ig Data' implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects. Style and approach This book takes a step-by-step approach to statistical analysis and machine learning, and is explained in a conversational and easy-to-follow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Real-world examples with sample code snippets are also included.

    • Computers

Mastering Predictive Analytics with Python


Author: Joseph Babcock
Publisher: Packt Publishing Ltd
ISBN: 1785889826
Category: Computers
Page: 334
View: 5673
Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn Gain an insight into components and design decisions for an analytical application Master the use Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover the advanced methods to analyze structured and unstructured data Find out how to deploy a machine learning model in a production environment Visualize the performance of models and the insights they produce Scale your solutions as your data grows using Python Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life Style and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.

    • Business & Economics

Predictive Analytics, Data Mining and Big Data

Myths, Misconceptions and Methods
Author: S. Finlay
Publisher: Springer
ISBN: 1137379286
Category: Business & Economics
Page: 260
View: 8770
This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.

    • Computers

Machine Learning with R


Author: Brett Lantz
Publisher: Packt Publishing Ltd
ISBN: 1782162151
Category: Computers
Page: 396
View: 3825
Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

    • Business & Economics

The Marketing Casebase

Short Examples of Marketing Practice
Author: Brian MacNamee,Ray McDonnell
Publisher: Cengage Learning Emea
ISBN: N.A
Category: Business & Economics
Page: 272
View: 2833
Marketing business handbook featuring examples of well-known and international companies and brands

    • Mathematics

Applying Data Science

Business Case Studies Using SAS
Author: Gerhard Svolba
Publisher: SAS Institute
ISBN: 163526054X
Category: Mathematics
Page: 490
View: 1457
See how data science can answer the questions your business faces! Applying Data Science: Business Case Studies Using SAS, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions. In eight entertaining and real-world case studies, Svolba combines data science and advanced analytics with business questions, illustrating them with data and SAS code. The case studies range from a variety of fields, including performing headcount survival analysis for employee retention, forecasting the demand for new projects, using Monte Carlo simulation to understand outcome distribution, among other topics. The data science methods covered include Kaplan-Meier estimates, Cox Proportional Hazard Regression, ARIMA models, Poisson regression, imputation of missing values, variable clustering, and much more! Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, Applying Data Science bridges the gap between high-level, business-focused books that skimp on the details and technical books that only show SAS code with no business context.