If you are interested in learning R and Data Science, but not interested in spending money on books, you are definitely in good space. There are a number of fantastic books and resources available online for free from top most creators and scientists. Here are such 13 best free (so far) online books and resources for learning R and Data Science from people like Hadley Wickham, Winston Chang, Garrett Grolemund and JHU Professor Roger Peng. R for Data Science, by Hadley Wickham and Garrett Grolemund, is a great book that introduces R programming, RStudio- the free and open-source integrated development environment for R, and the tidyverse, a suite of R packages designed by Wickham "to work together to make data science fast, fluent, and fun". Hadley Wickham wrote the book online and is available for free online at http://r4ds.had.co.nz/.
Almost a quarter century ago, a book was written about how organizations would focus on share of customer as opposed to share of market, building a personalized collaboration driven by big data. With advanced analytics, banking may finally getting close to realizing this vision. In 1993, a then revolutionary book, "The One to One Future: Building Relationships One Customer at a Time" was published, proposing the idea that as technology makes it affordable to track individual customers, marketing shifts from finding customers for products to finding products for customers. According to the authors, Don Peppers and Martha Rogers, Ph.D., a company could use technology to gather information about, and to communicate directly with, individuals to form a commercial bond. The book became a bestseller, and was on every marketer's bookshelf … almost a quarter century ago.
One of the ways I continue my learning is reading. I read for 30 minutes before hitting the bed every day. This not only makes sure that I learn some thing daily, but also ends my day in a fulfilling manner. Over the years, I have read a variety of books on various subjects. In this article, I will share a list of 7 must read books, which I think should be present in every Analyst's bookshelf.
One of my favourite books from 1997 is Ellen Ullman's collection of essays, Close to the Machine. Ullman began her adult life as an aspiring writer, diverted into software engineering for a decade or three, and from there found her way back into writing. Her 2012 novel, The Bug, recounted the travails of a programmer who spends a year trying to find and fix a single bug. In Life in Code: A Personal History of Technology, we discover where Ullman got the idea: this was her situation in one of the jobs she writes about in her section on programming. She was, she writes, deeply disappointed when the bug turned out to be a typo, found by reading reams -- literally, as she had it printed out on paper -- of computer code with great diligence.
Artificial intelligence (AI) and machine learning have long been part of PPC -- so why are AI and machine learning all of a sudden such hot topics? It is, in part, because exponential advances have now brought technology to the point where it can legitimately compete with the performance and precision of human account managers. I recently covered the new roles humans should play in PPC as automation takes over. In this post, I'll offer some ideas for what online marketing agencies should consider doing to remain successful in a world of AI-driven PPC management. According to the authors of the book "The Second Machine Age," chess master Garry Kasparov offered an interesting insight into how humans and computers should work together after he became the first chess champion to be defeated by a computer in 1997.
R B. Abhyankar Emphasizing theory and implementation issues more than specific applications and Prolog programming techniques, Computing with Logic Logic Programming with Prolog (The Benjamin Cummings Publishing Company, Menlo Park, Calif., 1988, 535 pp., $27 95) by David Maier and David S. Warren, respected researchers in logic programming, is a superb book Offering an in-depth treatment of advanced topics, the book also includes the necessary background material on logic and automatic theorem proving, making it self-contained. The only real prerequisite is a first course in data structures, although it would be helpful if the reader has also had a first course in program translation. The book has a wealth of exercises and would make an excellent textbook for advanced undergraduate or graduate students in computer science; it is also appropriate for programmers interested in the implementation of Prolog The book presents the concepts of logic programming using theory presentation, implementation, and application of Proplog, Datalog, and Prolog, three logic programming languages of increasing complexity that are based on horn clause subsets of propositional, predicate, and functional logic, respectively This incremental approach, unique to this book, is effective in conveying a thorough understanding of the subject The book consists of 12 chapters grouped into three parts (Part 1 chapters 1 to 3, Part 2. chapters 4 to 6, and Part 3 chapters 7 to 12), an appendix, and an index The three parts, each dealing with one of these logic programming languages, are organized the same First, the authors informally present the language using examples; an interpreter is also presented. Then the formal syntax and semantics for the language and logic are presented, along with soundness and completeness results for the logic and the effects of various search strategies Next, they give optimization techniques for the interpreter Each chapter ends with exercises, brief comments regarding the material in the chapter, and a bibliography Chapter I presents top-down and bottom-up interpreters for Proplog Chapter 2 offers a good discussion of the related notions: negation as failure, closed-world assumption, minimal models, and stratified programs Chapter 3 considers clause indexing and lazy concatenation as optimization techniques for the Proplog interpreter in chapter 1 Chapter 4 explains the connection between Datalog and relational algebra. Chapter 5 contains a proof of Herbrand's theorem for predicate logic.
Hence, at a coarse-grained level of abstraction, KB-Ss can be characterized in terms of two components: (1) a knowledge base, encoding the knowledge embodied by the system, and (2) a reasoning engine, which is able to query the knowledge base, infer or acquire knowledge from external sources, and add new knowledge to the knowledge base. A knowledge-level account of a KBS (that is, a competencecentered, implementation-independent description of a system), such as Clancey's (1985) analysis of first-generation rule-based systems, focuses on the task-centered competence of the system; that is, it addresses issues such as what kind of problems the KBS is designed to tackle, what reasoning methods it uses, and what knowledge it requires. In contrast with task-centered analyses, Levesque and Lakemeyer focus on the competence of the knowledge base rather than that of the whole system. Hence, their notion of competence is a task-independent one: It is the "abstract state of knowledge" (p. This is an interesting assumption, which the "proceduralists" in the AI community might object to: According to the procedural viewpoint of knowledge representation, the knowledge modeled in an application, its representation, and the associated knowledge-retrieval mechanisms have to be engineered as As a result, they would argue, it is not possible to discuss the knowledge of a system independently of the task context in which the system is meant to operate.
The Brain Makers: Genius, Ego, and Greed in the Quest for Machines That Think, Harvey P. Newquist, Sams Publishing, Indianapolis, Indiana, 1994, 488 pp., $24.95, ISBN 0-672-30412-0. Newquist is a business reporter who covered the field during the 1980s when academic researchers went commercial in one of the 1980's smaller speculative bubbles. His book begins with a history spanning Babbage to Turing to Minsky, McCarthy, Newell, Simon, Samuel, and others at the 1956 Dartmouth meeting and moves on to the 1980s, where the real story begins. Good, if glib, descriptions of people, places, and events are punctuated by technical explanations ranging from poor to inane. Because I am a little slow, it took me a quarter of the book to recognize a journalist with an attitude.
However, recently, there seems to be a new wave of interest, as indicated by many papers, monographs, edited books, and doctoral theses, in exploring aspects of similarity and analogical reasoning from various perspectives. Amid these numerous publications, Similarity and Analogical Reasoning surely stands out as the most valuable reference work on the topic, covering especially well the recent advances in the understanding of this topic, with many chapters written by leading researchers. Although it is based on a collection of papers initially presented at the Workshop on Similarity and Analogy, unlike the typical workshop proceedings, this volume is well edited and coherent in both its content and format, with a great deal of cross-references and detailed summary-comment chapters for every part of the book. Let us look at the book in detail. Because each of these chapters has a different perspective, approach, and organization, I first discuss a number of chapters one by one.
How do we form categories? What is the role of similarity in categorization? Can we formalize the answers to these questions to derive further insights and develop useful software systems? These were the questions addressed at an interdisciplinary meeting attended by psychologists, computer scientists, anthropologists, statisticians, and philosophers held at the University of Edinburgh. The edited volume Similarity and Categorization arises from this meeting. The publication of Similarity and Categorization is timely because the study of categorization is at a theoretical crossroads.