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- Practical Text Analytics - Maximizing the Value of Text Data | Murugan Anandarajan | Springer
- Practical Data Science
Machine learning in the last few decades has given way to an AI revolution. As the amount of data produced continues to scale, use of algorithms enables faster computational understanding to create new opportunities for innovation. From self-driving cars to virtual assistants, the possibilities for these developing technologies are endless. Download paper.
Deploying smart systems in ways we humans find natural and intuitive is both science and the art.
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- A Practical Introduction in R;
This e-book explores the current boundaries of AI, as well as the many ways that modern AI applications can improve our understanding of the world and enable us to make better, faster decisions. Download e-book. Making Sense of AI. Enabling choice and control Orchestrate your efforts with an integrated platform covering all stages of the analytics life cycle The Talent Dividend Analytics talent is driving competitive advantage at data-oriented companies.
The Use of Open Source is Growing. Insights from a presentation at the Hadoop Summit Three's Company: The Benefits of Forecasting, Optimization and Simulation Practical advice for applying advanced analytics in hospitality and gaming. Any Time. Data for Good Doing good with government data Improving health, protecting the vulnerable, preventing fraud — and more Using data to change the world How organizations are harnessing data to help solve some of the world's most challenging issues Why choose a career in analytics?
Job security, a larger salary, a chance to do good and more. This is everything a graduate student could ask for in a text.
Applying Analytics: A Practical Introduction
A guide to practical data mining, collective intelligence, and building recommendation systems by Ron Zacharski. This work is licensed under a Creative Commons license. For final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.
The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers many more cutting-edge data mining topics. Offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This book aims to get you into data mining quickly. Load some data e. The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond.
Modeling with Data offers a useful blend of data-driven statistical methods and nuts-and-bolts guidance on implementing those methods. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you concepts behind neural networks and deep learning.
Using this approach, you can reach effective solutions in small increments. A clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts in social media mining.
This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. Learn how to use a problem's "weight" against itself. Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.
Its function is something like a traditional textbook — it will provide the detail and background theory to support the School of Data courses and challenges.
Practical Text Analytics - Maximizing the Value of Text Data | Murugan Anandarajan | Springer
MapReduce  is a programming model for expressing distributed computations on massive amounts of data and an execution framework for large-scale data processing on clusters of commodity servers. It was originally developed by Google It aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical. Intro to Hadoop - An open-source framework for storing and processing big data in a distributed environment across clusters of computers using simple programming models.
It is designed to scale up from single servers to thousands of machines. This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.
The Data Science Handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you. Useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. Practical programming for total beginners. In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.
This is a hands-on guide to Python 3 and its differences from Python 2. Each chapter starts with a real, complete code sample, picks it apart and explains the pieces, and then puts it all back together in a summary at the end. The first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know to analyze their own data using the R language. Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples.
I Dani started teaching the introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. These are my own notes for the class which were trans-coded to book form. Introduction to computer science using the Python programming language.
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It covers the basics of computer programming in the first part while later chapters cover basic algorithms and data structures. This is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever.
Practical Data Science
After all, everybody has to start somewhere. This book is NOT introductory. The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied. If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket.
Packed with practical recipes written and tested with Python 3. For experienced Python developers. This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. This is a simple book to learn the Python programming language, it is for the programmers who are new to Python.
This book describes Python, an open-source general-purpose interpreted programming language available for a broad range of operating systems. This book describes primarily version 2, but does at times reference changes in version 3. The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R.
It is supposed to be organized by task but not by discipline. We try to make a cross-disciplinary book, i. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code.
My intent is to present a relatively brief, non-jargony overview of how practicing epidemiologists can apply some of the extremely powerful spatial analytic tools that are easily available to them. By taking you through the development of a real web application from beginning to end, this hands-on guide demonstrates the practical advantages of test-driven development TDD with Python. An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. This hands-on guide takes you through Python a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design.
Updated to Python 3. This is an introduction to the basic concepts of linear algebra, along with an introduction to the techniques of formal mathematics. It has numerous worked examples, exercises and complete proofs, ideal for independent study.