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Make a suggestion Refine your searchData analysis with R / Tony Fischetti
Title : Data analysis with R : a comprehensive guide to manipulating, analyzing, and visualizing data in R Material Type: printed text Authors: Tony Fischetti, Author Edition statement: 2nd edition Publisher: Birmingham : Packt Publishing Publication Date: 2018 Pagination: vii, 554 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-7883-9372-0 General note: Includes index (p. 449-554) Languages : English (eng) Original Language : English (eng) Descriptors: R (Computer program language)
Statistics - Data processingClass number: 519.50285 Abstract: About This Book Analyze your data using R - the most powerful statistical programming language Learn how to implement applied statistics using practical use-cases Use popular R packages to work with unstructured and structured data Who This Book Is For Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book. What You Will Learn Gain a thorough understanding of statistical reasoning and sampling theory Employ hypothesis testing to draw inferences from your data Learn Bayesian methods for estimating parameters Train regression, classification, and time series models Handle missing data gracefully using multiple imputation Identify and manage problematic data points Learn how to scale your analyses to larger data with Rcpp, data.table, dplyr, and parallelization Put best practices into effect to make your job easier and facilitate reproducibility In Detail Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst. Style and approach An easy ... Contents note: RefresheR; The Shape of Data; Describing Relationships; Probability; Using Data To Reason About The World; Testing Hypotheses; Bayesian Methods; The Bootstrap; Predicting Continuous Variables; Predicting Changes with time; Sources of Data; Dealing with Missing Data; Dealing with Messy Data; Dealing with Large Data; Working with Popular R Packages; Reproducibility and Best Practices; Index Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=19065 Hold
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Barcode Call number Media type Location Section Status 1702-002380 519.50285 Fis-Dat 2018 General Collection SEEU Library Skopje English Available 1702-002379 519.50285 Fis-Dat 2018 General Collection SEEU Library Skopje English Due for return by 12/31/2020
Title : PHP Web 2.0 : Mashup Projects : create practical mashups in PHP grabbing and mixing data from Google Maps, Flickr, Amazon, YouTube, MSN Search, Yahoo!, Last.fm, and 411Sync.com Material Type: printed text Authors: Shu-Wai Chow, Author Publisher: Birmingham : Packt Publishing Publication Date: 2007 Pagination: v, 283 p. Size: 24 cm. ISBN (or other code): 978-1-84719-088-8 Price: 39.99 $ General note: Includes index(p.[279]-283) Languages : English (eng) Original Language : English (eng) Descriptors: PHP (Computer program language)
Web sites - DesignClass number: 005.13 Abstract: A mashup is a web page or application that combines data from two or more external online sources into an integrated experience. This book is your entryway to the world of mashups and Web 2.0. You will create PHP projects that grab data from one place on the Web, mix it up with relevant information from another place on the Web and present it in a single application.
This book is made up of five real-world PHP projects. Each project begins with an overview of the technologies and protocols needed for the project, and then dives straight into the tools used and details of creating the project:
* Look up products on Amazon.Com from their code in the Internet UPC database
* A fully customized search engine with MSN Search and Yahoo!
* A personal video jukebox with YouTube and Last.FM
* Deliver real-time traffic incident data via SMS and the California Highway Patrol!
* Display pictures sourced from Flickr in Google maps
All the mashup applications used in the book are built upon free tools and are thoroughly explained. You will find all the source code used to build the mashups used in this book in the code download section for this book.Contents note: Introduction to Mashups; Buy it on Amazon; Make Your Own Search Engine; Your Own Video Jukebox; Traffic Incidents via SMS; London Tube Photos; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=14102 Hold
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Barcode Call number Media type Location Section Status 1702-000695 005.13 Cho-PHP 2007 General Collection SEEU Library Skopje English Due for return by 11/18/2020
Title : Building Machine Learning Systems with Python : Master the art of machine learning with Python and build effective machine learning systems with this intensive hands-on guide Material Type: printed text Authors: Willi Richert, Author ; Luis Pedro Coelho, Author Publisher: Birmingham : Packt Publishing Publication Date: 2013 Pagination: vi, 271 p. Size: 24 cm ISBN (or other code): 978-1-7821-6140-0 General note: Includes index (p. [265]-271) Languages : English (eng) Original Language : English (eng) Descriptors: Computer programming Class number: 005.1 Abstract: Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python. Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on data sets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail. Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques. Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on. Readers will learn how to write programs that classify the quality of Stack Overflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text’s most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects. Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems. Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=16027 Copies
Barcode Call number Media type Location Section Status 1702-001577 005.1 Ric-Bui 2013 General Collection Library "Max van der Stoel" English Not for loan
Title : Python machine learning cookbook : 100 recipes that teach you how to perform various machine learning tasks in the real world Material Type: printed text Authors: Prateek Joshi, Author Publisher: Birmingham : Packt Publishing Publication Date: 2016 Pagination: x, 286 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-7864-6447-7 General note: Includes index Languages : English (eng) Original Language : English (eng) Descriptors: Machine learning
Python (Computer program language)Class number: 006.31 Abstract: With Python Machine Learning Cookbook, you will learn how to perform carious machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithm to solve real-world problems and use Python to implement these algorithms.
You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithm, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-worlds examples.Contents note: The realm of supervised learning; Constructing a classifier; Predictive modeling; Clustering with unsupervised learning; Building recommendation engines; Analyzing text data; Speech recognition; Dissecting time series and sequential data; Image content analysis; Biometric face recognition; Deep neural networks; Visualizing data; Index Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17508 Copies
Barcode Call number Media type Location Section Status 1702-002415 006.31 Jos-Pyt 2016 General Collection SEEU Library Skopje English Not for loan
Title : Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow Material Type: printed text Authors: Sebastian Raschka, Author ; Vahid Mirjalili, Author Edition statement: 2 nd Publisher: Birmingham : Packt Publishing Publication Date: 2017 Pagination: xviii-595 p Layout: ill Size: 24 cm ISBN (or other code): 978-1-7871-2593-3 General note: Includes index. Languages : English (eng) Original Language : English (eng) Descriptors: COMPUTERS - Intelligence (AI) & Semantics;
Machine learning
Python (Computer program language)Class number: 005.133 Abstract: "Python Machine learning" is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world.Contents note: Giving computers the ability to learn from data; Training simple machine learning algorithms from classification; A tour of machine learning classifiers using scikit-learn; Building good training sets; Compressing data via dimensionality reduction; Learning best practices for model evaluation and hyperparameter tuning; Combining different models for ensemble learning; Applying machine learning to sentiment analysis; Embedding a machine learning model into a web application; Predicting continuous target variables with regression analysis; Working with unlabeled data; Implementing a multilayer artificial neural network from scratch; Parallelizing neural network training with tensorflow; Going deeper; Classifying images with deep convolutional neural networks; Modeling sequential data recurrent neural networks; Index Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17509 Copies
Barcode Call number Media type Location Section Status 1702-002411 005.133 Ras-Pyt 2017 General Collection SEEU Library Skopje English Not for loan
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