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Get to Know Us. Customer Service. This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. This book gives a very quick but still thorough introduction to reinforcement learning, and includes algorithms for quite a few methods.
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This is everything a graduate student could ask for in a text. 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.
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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.
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Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development. Learn about Cloudera Impala--an open source project that's opening up the Apache Hadoop software stack to a wide audience of database analysts, users, and developers. 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.
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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.
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.