Big data
Matthew Kirk
Ten praktyczny przewodnik pozwoli osiągnąć biegłość w stosowaniu uczenia maszynowego w codziennej pracy. Autor, Matthew Kirk, bez akademickich rozważań pokazuje, jak integrować i testować algorytmy uczenia maszynowego w swoim kodzie. Książka przedstawia wykorzystanie testów z użyciem bibliotek naukowych NumPy, Pandas, Scikit-Learn oraz SciPy dla języka Python, ilustrując je licznymi wykresami oraz przykładami kodu. Książka ta pomoże programistom i analitykom biznesowym zainteresowanym badaniem danych w: Zapoznaniu się z rzeczywistymi przykładami testowania poszczególnych algorytmów poprzez zajmujące ćwiczenia praktyczne. Stosowaniu programowania sterowanego testami do pisania i uruchamiania testów przed rozpoczęciem kodowania. Badaniu technik poprawiających nasze modele uczenia maszynowego poprzez wydobywanie danych i opracowywanie funkcjonalności. Zwracaniu uwagi na ryzyka związane z uczeniem maszynowym takie jak niedopasowanie danych. Pracy z algorytmem K najbliższych sąsiadów, sieciami neuronowymi, klastrami i innymi technikami. Matthew Kirk jest konsultantem, autorem i międzynarodowym prelegentem, specjalizującym się w uczeniu maszynowym i analizie danych z wykorzystaniem języków Ruby i Python. Mieszka w Seattle i lubi pomagać innym programistom w integrowaniu analizy danych ze stosowanymi przez nich technologiami. Więcej zasobów dotyczących uczenia maszynowego można znaleźć pod adresem www.matthewkirk.com.
Michael Heydt
Python Web Scraping Cookbook is a solution-focused book that will teach you techniques to develop high-performance scrapers and deal with crawlers, sitemaps, forms automation, Ajax-based sites, caches, and more. You'll explore a number of real-world scenarios where every part of the development/product life cycle will be fully covered. You will not only develop the skills needed to design and develop reliable performance data flows, but also deploy your codebase to AWS. If you are involved in software engineering, product development, or data mining (or are interested in building data-driven products), you will find this book useful as each recipe has a clear purpose and objective. Right from extracting data from the websites to writing a sophisticated web crawler, the book's independent recipes will be a godsend. This book covers Python libraries, requests, and BeautifulSoup. You will learn about crawling, web spidering, working with Ajax websites, paginated items, and more. You will also learn to tackle problems such as 403 errors, working with proxy, scraping images, and LXML.By the end of this book, you will be able to scrape websites more efficiently and able todeploy and operate your scraper in the cloud.
Katharine Jarmul
The Internet contains the most useful set of data ever assembled, most of which is publicly accessible for free. However, this data is not easily usable. It is embedded within the structure and style of websites and needs to be carefully extracted. Web scraping is becoming increasingly useful as a means to gather and make sense of the wealth of information available online.This book is the ultimate guide to using the latest features of Python 3.x to scrape data from websites. In the early chapters, you'll see how to extract data from static web pages. You'll learn to use caching with databases and files to save time and manage the load on servers. Aftercovering the basics, you'll get hands-on practice building a more sophisticated crawler using browsers, crawlers, and concurrent scrapers.You'll determine when and how to scrape data from a JavaScript-dependent website using PyQt and Selenium. You'll get a better understanding of how to submit forms on complex websites protected by CAPTCHA. You'll find out how to automate these actions with Python packages such as mechanize. You'll also learn how to create class-based scrapers with Scrapy libraries and implement your learning on real websites.By the end of the book, you will have explored testing websites with scrapers, remote scraping, best practices, working with images, and many other relevant topics.
Yuxi (Hayden) Liu
Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use.With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game.By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems.
Sherin Thomas, Sudhanshu Passi
PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with PyTorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly.PyTorch Deep Learning Hands-On shows how to implement the major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools.Each chapter focuses on a different area of deep learning. Chapters start with a refresher on how the model works, before sharing the code you need to implement it in PyTorch.This book is ideal if you want to rapidly add PyTorch to your deep learning toolset.
Ben Mearns, Alex Mandel, Alexander Bruy, Anita...
The first module Learning QGIS, Third edition covers the installation and configuration of QGIS. You’ll become a master in data creation and editing, and creating great maps. By the end of this module, you’ll be able to extend QGIS with Python, getting in-depth with developing custom tools for the Processing Toolbox. The second module QGIS Blueprints gives you an overview of the application types and the technical aspects along with few examples from the digital humanities. After estimating unknown values using interpolation methods and demonstrating visualization and analytical techniques, the module ends by creating an editable and data-rich map for the discovery of community information. The third module QGIS 2 Cookbook covers data input and output with special instructions for trickier formats. Later, we dive into exploring data, data management, and preprocessing steps to cut your data to just the important areas. At the end of this module, you will dive into the methods for analyzing routes and networks, and learn how to take QGIS beyond the out-of-the-box features with plug-ins, customization, and add-on tools. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: ? Learning QGIS, Third Edition by Anita Graser ? QGIS Blueprints by Ben Mearns ? QGIS 2 Cookbook by Alex Mandel, Víctor Olaya Ferrero, Anita Graser, Alexander Bruy
Henric Cronström, Ferran Garcia Pagans, Neeraj Kharpate,...
Qlik Sense is powerful and creative visual analytics software that allows users to discover data, explore it, and dig out meaningful insights in order to make a profit and make decisions for your business. This course begins by introducing you to the features and functions of the most modern edition of Qlik Sense so you get to grips with the application. The course will teach you how to administer the data architecture in Qlik Sense, enabling you to customize your own Qlik Sense application for your business intelligence needs. It also contains numerous recipes to help you overcome challenging situations while creating fully featured desktop applications in Qlik Sense. It explains how to combine Rattle and Qlik Sense Desktop to apply predictive analytics to your data to develop real-world interactive data applications. The course includes premium content from three of our most popular books:[*] Learning Qlik Sense: The Official Guide Second Edition[*] Qlik Sense Cookbook[*] Predictive Analytics using Rattle and Qlik SenseOn completion of this course, you will be self-sufficient in improving your data analysis and will know how to apply predictive analytics to your datasets. Through this course, you will be able to create predictive models and data applications, allowing you to explore your data insights much deeper.
Pablo Labbe, Philip Hand, Neeraj Kharpate
Qlik Sense allows you to explore simple and complex data to reveal hidden insight and data relationships that help you make quality decisions for overall productivity. An expert Qlik Sense user can use its features for business intelligence in an enterprise environment effectively. Qlik Sense Cookbook is an excellent guide for all aspiring Qlik Sense developers and will empower you to create featured desktop applications to obtain daily insights at work.This book takes you through the basics and advanced functions of Qlik Sense February 2018 release. You’ll start with a quick refresher on obtaining data from data files and databases, and move on to some more refined features including visualization, and scripting, as well as managing apps and user interfaces. You will then understand how to work with advanced functions like set analysis and set expressions. As you make your way through this book, you will uncover newly added features in Qlik Sense such as new visualizations, label expressions and colors for dimension and measures.By the end of this book, you will have explored various visualization extensions to create your own interactive dashboard with the required tips and tricks. This will help you overcome challenging situations while developing your applications in Qlik Sense.
Miguel Garc?É?íÂ!!=a, Barry Harmsen, Miguel Angel Garcia
Business Intelligence technologies are a must-have in every business to make informed decisions and keep up-to speed with the ever-evolving markets. QlikView's disruptive technology makes it a key player and leader in the industry; with its flexibility and powerful analytics environment, building QlikView apps can be mastered by both, business users as well as developers.This book will help you learn QlikView Development from a basic to a practitioner level using a step-by-step approach in a practical environment, and apply proven best practices on each topic.Throughout the book, we will build a QlikView app based on real data about Airline Operations that will help HighCloud Airlines make informed business decisions and analysis-guided strategies. HighCloud Airlines executives are evaluating if entering the US market is a good strategy and, if so, which line of business should they focus their investments on; they need QlikView to make the best decision.The application will be evolving chapter by chapter, along with your skills, going from a simple proof of concept to creating a Data Model, adding a custom style, building a Dashboard and handling and manipulating the source data via script. We will meet the HighCloud Airlines requirement by using many different data visualization objects and time-saving techniques.The whole application uses real data taken from the Bureau of Transportations statistics of the US and encompasses the operations of Airlines both domestic and international. With three years worth of data, you will help HighCloud Airlines discover where people travel the most, which are the Carriers with the most market share, what is the average load factor per airline, which aircraft is the most used to perform flights, which are the busiest airports, and a whole universe of new insights.
Miguel Angel Garcia, Barry Harmsen, Stephen Redmond,...
QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations.Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources.With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro.This Learning Path includes content from the following Packt products:• QlikView for Developers by Miguel Ángel García, Barry Harmsen • Mastering QlikView by Stephen Redmond • Mastering QlikView Data Visualization by Karl Pover
Stephen Redmond
QlikView has been around since 1993, but has only really taken off in recent years as a leader in the in-memory BI space and, more recently, in the data discovery area. QlikView features the ability to consolidate relevant data from multiple sources into a single application, as well as an associative data model to allow you to explore the data to a way your brain works, state-of-the-art visualizations, dashboard, analysis and reports, and mobile data access.QlikView for Developers Cookbook builds on your initial training and experiences with QlikView to help you become a better developer. This book features plenty of hands-on examples of many challenging functions.Assuming a basic understanding of QlikView development, this book provides a range of step-by-step exercises to teach you different subjects to help build your QlikView developer expertise.From advanced charting and layout to set analysis; from advanced aggregations through to scripting, performance, and security, this book will cover all the areas that you need to know about.The recipes in this book will give you a lot of the information that you need to become an excellent QlikView developer.
Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
Antoine Jacquier, Oleksiy Kondratyev, Alexander Lipton, Marcos...
With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware.Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware.This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm.This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun!
Dan MacLean
Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples.This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse.By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
R: Data Analysis and Visualization. Click here to enter text
Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi...
The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module!This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility.The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework.With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs.The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions.Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on.
Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan
Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data.This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way.By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.
R Data Analysis Projects. Build end to end analytics systems to get deeper insights from your data
Gopi Subramanian
R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle.You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively.By the end of this book, you’ll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.
R Data Mining. Implement data mining techniques through practical use cases and real-world datasets
Andrea Cirillo
R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R.It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data.Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
R Data Science Essentials. R Data Science Essentials
Raja B. Koushik, Sharan Kumar Ravindran
With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards.By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Atmajitsinh Gohil, Atmajitsingh Gohil
Vitor Bianchi Lanzetta
R is an open source language for data analysis and graphics that allows users to load various packages for effective and better data interpretation. Its popularity has soared in recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions.This book is an update to our earlier R data visualization cookbook with 100 percent fresh content and covering all the cutting edge R data visualization tools. This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization using R. It starts off with the basics of ggplot2, ggvis, and plotly visualization packages, along with an introduction to creating maps and customizing them, before progressively taking you through various ggplot2 extensions, such as ggforce, ggrepel, and gganimate. Using real-world datasets, you will analyze and visualize your data as histograms, bar graphs, and scatterplots, and customize your plots with various themes and coloring options. The book also covers advanced visualization aspects such as creating interactive dashboards using ShinyBy the end of the book, you will be equipped with key techniques to create impressive data visualizations with professional efficiency and precision.
R Deep Learning Cookbook. Solve complex neural net problems with TensorFlow, H2O and MXNet
PKS Prakash, Achyutuni Sri Krishna Rao
Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance.By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Mark Hodnett, Joshua F. Wiley
Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem.This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics.By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
R Deep Learning Projects. Master the techniques to design and develop neural network models in R
Yuxi (Hayden) Liu, Pablo Maldonado
R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains.This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.
Yu-Wei, Chiu (David Chiu)
This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.