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Ultimate Guide to Link Building : How to Build Website Authority, Increase Traffic and Search Ranking with Backlinks
Link. Rank. Profit.Web marketing pioneers Eric Ward and Garrett French explain the complicated world of link building and the art of backlinks in this second edition of their groundbreaking guide to the business of web links. The web has changed from a web of things to a web of people. And it's all about connections, about the way we're all linked together by one thing or another.From content development and integrated marketing techniques to purely tactical link bait, this is a comprehensive guide, with examples and templates, from the masters of link marketing. Link building expert Eric Ward and online marketer Garrett French teach you how to wisely:Execute a link audit and competitor analysisDevelop a structured, long-term link-building strategyIdentify and approach quality, top-ranking websites with a value propositionDifferentiate links for traffic from links for rankingKeep on the right side of search engine guidelines
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Healthcare Big Data Analytics : Computational Optimization and Cohesive Approaches
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Machine Learning Pocket Reference : Working with Structured Data in Python
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning.Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data.You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover:Classification, using the Titanic datasetCleaning data and dealing with missing dataExploratory data analysisCommon preprocessing steps using sample dataSelecting features useful to the modelModel selectionMetrics and classification evaluationRegression examples using k-nearest neighbor, decision trees, boosting, and moreMetrics for regression evaluationClusteringDimensionality reductionScikit-learn pipelines
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An Introduction to Optimization with Applications in Machine Learning and Data Analytics
The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers.Providing students with a marketable skill set requires topics from many areas of optimization.The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business.Optimization reaches into many different fields. This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math.The book represents an attempt at overcoming this imbalance for all students taking such a course. The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitions and proving theorems.Optimization techniques are at the heart of the first spam filters, are used in self-driving cars, play a great role in machine learning, and can be used in such places as determining a batting order in a Major League Baseball game.Additionally, optimization has seemingly limitless other applications in business and industry.In short, knowledge of this subject offers an individual both a very marketable skill set for a wealth of jobs as well as useful tools for research in many academic disciplines. Many of the problems rely on using a computer. Microsoft’s Excel is most often used, as this is common in business, but Python and other languages are considered.The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB® or Mathematica, and the computer science students to write their own programs in Java or Python.
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Social Data Analytics
This book is an introduction to social data analytics along with its challenges and opportunities in the age of Big Data and Artificial Intelligence.It focuses primarily on concepts, techniques and methods for organizing, curating, processing, analyzing, and visualizing big social data: from text to image and video analytics.It provides novel techniques in storytelling with social data to facilitate the knowledge and fact discovery.The book covers a large body of knowledge that will help practitioners and researchers in understanding the underlying concepts, problems, methods, tools and techniques involved in modern social data analytics.It also provides real-world applications of social data analytics, including: Sales and Marketing, Influence Maximization, Situational Awareness, customer success and Segmentation, and performance analysis of the industry.It provides a deep knowledge in social data analytics by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions.Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in data science to graduate courses in data analytics.
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Data Science and Analytics
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Data Analytics Initiatives : Managing Analytics for Success
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