Rule Based Systems for Big Data: A Machine Learning Approach (Repost)

Han Liu, Alexander Gegov, Mihaela Cocea, "Rule Based Systems for Big Data: A Machine Learning Approach"


English | 2015 | ISBN-10: 3319236954 | 121 pages | pdf | 2.8 MB

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data.




The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems

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Data Mining For Dummies (repost)

Meta S. Brown, "Data Mining For Dummies"


2014 | ISBN: 1118893174 | 408 pages | PDF | 10 MB

Delve into your data for the key to success


Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business"s entire paradigm for a more successful outcome.




Data Mining for Dummies shows you why it doesn"t take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business"s needs. In this book, you"ll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including:




Model creation, validity testing, and interpretation


Effective communication of findings


Available tools, both paid and open-source


Data selection, transformation, and evaluation


Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You"ll gain the confidence you need to start making data mining practices a routine part of your successful business. If you"re serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining.

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Mastering Python for Data Science

Mastering Python for Data Science by Samir


English | 31 Aug. 2015 | ISBN: 1784390151 | 294 Pages | EPUB/MOBI/PDF (True) | 30.1 MB

If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.




Explore the world of data science through Python and learn how to make sense of data




About This Book




Master data science methods using Python and its libraries


Create data visualizations and mine for patterns


Advanced techniques for the four fundamentals of Data Science with Python – data mining, data analysis, data visualization, and machine learning




What You Will Learn




Manage data and perform linear algebra in Python


Derive inferences from the analysis by performing inferential statistics


Solve data science problems in Python


Create high-end visualizations using Python


Evaluate and apply the linear regression technique to estimate the relationships among variables.


Build recommendation engines with the various collaborative filtering algorithms


Apply the ensemble methods to improve your predictions


Work with big data technologies to handle data at scale




In Detail




Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.




This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.




Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods.




Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.




Style and approach




This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

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