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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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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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Learning Analytics in the Classroom : Translating Learning Analytics Research for Teachers
Learning Analytics in the Classroom presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different education domains.Highlighting the real potential of learning analytics as a way to better understand and enhance student learning and with each chapter including specific discussion about what the research means in the classroom, this book provides educators and researchers alike with the tools and frameworks to effectively make sense of and use data and analytics in their everyday practice. This volume is split into five sections, all of which relate to the key themes in understanding learning analytics through the lens of the classroom: broad theoretical perspectivesunderstanding learning through analyticsthe relationship between learning design and learning analyticsanalytics in the classroom and the impact it can and will have on educationimplementing analytics and the challenges involved. Bridging the gap between research, theory and practice, Learning Analytics in the Classroom is both a practical tool and an instructive guide for educators, and a valuable addition to researchers' bookshelves.A team of world-leading researchers and expert editors have compiled a state-of-the-art compendium on this fascinating subject and this will be a critical resource for the evolution of this field into the future.
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Honest Seo : Demystifying the Google Algorithm to Help You Get More Traffic and Revenue
SEO is confusing—but it doesn’t have to be. With SEO, there’s so much misinformation out there.There are also a lot of inexperienced practitioners giving not-so-great (and often not-so-honest) advice.This can be complicated, overwhelming, and expensive, leaving you feeling indecisive and not knowing where to start or who to believe. That’s where Honest SEO, a book that’s been over 20 years in the making, comes in.Jason Hennessey—after spending decades reverse engineering the Google algorithm—shares the proven strategies he’s used to earn more than $1 billion for his clients by leveraging the power of SEO. And you don’t have to know how to code or have a computer science degree to read and understand this book.With Honest SEO, you can learn the fundamentals that won’t go out of style when Google releases a new algorithm update.No matter what business you’re in, even if you don’t know where to start or what to focus on to actually get results, Honest SEO is the digital marketing blueprint written with perennial cutting-edge strategies that’ll increase your Google rankings, traffic, leads, and sales, all with the goal of increasing your revenue.
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Marketing Analytics : A Machine Learning Approach
With businesses becoming ever more competitive, marketing strategies need to be more precise and performance oriented.Companies are investing considerably in analytical infrastructure for marketing.This new volume, Marketing Analytics: A Machine Learning Approach, enlightens readers on the application of analytics in marketing and the process of analytics, providing a foundation on the concepts and algorithms of machine learning and statistics.The book simplifies analytics for businesses and explains its uses in different aspects of marketing in a way that even marketers with no prior analytics experience will find it easy to follow, giving them to tools to make better business decisions. This volume gives a comprehensive overview of marketing analytics, incorporating machine learning methods of data analysis that automates analytical model building.The volume covers the important aspects of marketing analytics, including segmentation and targeting analysis, statistics for marketing, marketing metrics, consumer buying behavior, neuromarketing techniques for consumer analytics, new product development, forecasting sales and price, web and social media analytics, and much more. This well-organized and straight-forward volume will be valuable for marketers, managers, decision makers, and research scholars, and faculty in business marketing and information technology and would also be suitable for classroom use.
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Machine Learning : A Bayesian and Optimization Perspective
Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification.The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees.It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering.Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures.The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition.The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes.
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Stochastic Optimization for Large-scale Machine Learning
Advancements in the technology and availability of data sources have led to the `Big Data' era.Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models.One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge.Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features:Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
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CLIL : Content and Language Integrated Learning
CLIL has emerged since the millennium as a major trend in education.The book draws on the authors' experiences of CLIL in primary, secondary and English language schools across Europe.It summarizes the theory which underpins the teaching of a content subject through another language and discusses its practical application, outlining the key directions for the development of research and practice.The book acknowledges the uncertainty many teachers feel about CLIL, because of the requirement for both language and subject knowledge, while providing theoretical and practical routes towards successful practice for all.It is essential reading for both language and content-subject teachers.
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