Mastering Machine Learning with R

Author: Cory Lesmeister

Publisher: Packt Publishing Ltd

ISBN: 1787284484

Category: Computers

Page: 420

View: 6356


Master machine learning techniques with R to deliver insights in complex projects About This Book Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning Implement advanced concepts in machine learning with this example-rich guide Who This Book Is For This book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field. What You Will Learn Gain deep insights into the application of machine learning tools in the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Master fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Learn how to use R in a cloud service such as Amazon In Detail This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets. Style and approach The book delivers practical and real-world solutions to problems and a variety of tasks such as complex recommendation systems. By the end of this book, you will have gained expertise in performing R machine learning and will be able to build complex machine learning projects using R and its packages.

Mastering Machine Learning with Python in Six Steps

A Practical Implementation Guide to Predictive Data Analytics Using Python

Author: Manohar Swamynathan

Publisher: Apress

ISBN: 1484228669

Category: Computers

Page: 358

View: 6425


Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You'll Learn Examine the fundamentals of Python programming language Review machine Learning history and evolution Understand machine learning system development frameworks Implement supervised/unsupervised/reinforcement learning techniques with examples Explore fundamental to advanced text mining techniques Implement various deep learning frameworks Who This Book Is For Python developers or data engineers looking to expand their knowledge or career into machine learning area. Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python. Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning.

R: Unleash Machine Learning Techniques

Author: Raghav Bali,Dipanjan Sarkar,Brett Lantz,Cory Lesmeister

Publisher: Packt Publishing Ltd

ISBN: 1787128288

Category: Computers

Page: 1123

View: 6961


Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media. This Learning Path has been curated from three Packt products: R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar Machine Learning with R Learning - Second Edition By Brett Lantz Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

R in a Nutshell

Author: Joseph Adler

Publisher: O'Reilly Germany

ISBN: 3897216507

Category: Computers

Page: 768

View: 7159


Wozu sollte man R lernen? Da gibt es viele Gründe: Weil man damit natürlich ganz andere Möglichkeiten hat als mit einer Tabellenkalkulation wie Excel, aber auch mehr Spielraum als mit gängiger Statistiksoftware wie SPSS und SAS. Anders als bei diesen Programmen hat man nämlich direkten Zugriff auf dieselbe, vollwertige Programmiersprache, mit der die fertigen Analyse- und Visualisierungsmethoden realisiert sind – so lassen sich nahtlos eigene Algorithmen integrieren und komplexe Arbeitsabläufe realisieren. Und nicht zuletzt, weil R offen gegenüber beliebigen Datenquellen ist, von der einfachen Textdatei über binäre Fremdformate bis hin zu den ganz großen relationalen Datenbanken. Zudem ist R Open Source und erobert momentan von der universitären Welt aus die professionelle Statistik. R kann viel. Und Sie können viel mit R machen – wenn Sie wissen, wie es geht. Willkommen in der R-Welt: Installieren Sie R und stöbern Sie in Ihrem gut bestückten Werkzeugkasten: Sie haben eine Konsole und eine grafische Benutzeroberfläche, unzählige vordefinierte Analyse- und Visualisierungsoperationen – und Pakete, Pakete, Pakete. Für quasi jeden statistischen Anwendungsbereich können Sie sich aus dem reichen Schatz der R-Community bedienen. Sprechen Sie R! Sie müssen Syntax und Grammatik von R nicht lernen – wie im Auslandsurlaub kommen Sie auch hier gut mit ein paar aufgeschnappten Brocken aus. Aber es lohnt sich: Wenn Sie wissen, was es mit R-Objekten auf sich hat, wie Sie eigene Funktionen schreiben und Ihre eigenen Pakete schnüren, sind Sie bei der Analyse Ihrer Daten noch flexibler und effektiver. Datenanalyse und Statistik in der Praxis: Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik lernen Sie, wie Sie Daten aufbereiten, mithilfe der Grafikfunktionen des lattice-Pakets darstellen, statistische Tests durchführen und Modelle anpassen. Danach werden Ihnen Ihre Daten nichts mehr verheimlichen.

Mastering Machine Learning with scikit-learn

Author: Gavin Hackeling

Publisher: Packt Publishing Ltd

ISBN: 1788298497

Category: Computers

Page: 254

View: 579


Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks In Detail Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn's API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model's performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.

Mastering Data Analysis with R

Author: Gergely Daroczi

Publisher: Packt Publishing Ltd

ISBN: 1783982039

Category: Computers

Page: 396

View: 1892


Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Machine Learning mit Python

Das Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning

Author: Sebastian Raschka

Publisher: MITP-Verlags GmbH & Co. KG

ISBN: 3958454240

Category: Computers

Page: 424

View: 4812


R für Dummies

Author: Andrie de Vries,Joris Meys

Publisher: John Wiley & Sons

ISBN: 3527812520

Category: Computers

Page: 414

View: 4469


Wollen Sie auch die umfangreichen Möglichkeiten von R nutzen, um Ihre Daten zu analysieren, sind sich aber nicht sicher, ob Sie mit der Programmiersprache wirklich zurechtkommen? Keine Sorge - dieses Buch zeigt Ihnen, wie es geht - selbst wenn Sie keine Vorkenntnisse in der Programmierung oder Statistik haben. Andrie de Vries und Joris Meys zeigen Ihnen Schritt für Schritt und anhand zahlreicher Beispiele, was Sie alles mit R machen können und vor allem wie Sie es machen können. Von den Grundlagen und den ersten Skripten bis hin zu komplexen statistischen Analysen und der Erstellung aussagekräftiger Grafiken. Auch fortgeschrittenere Nutzer finden in diesem Buch viele Tipps und Tricks, die Ihnen die Datenauswertung erleichtern.

Machine Learning Algorithms

Author: Giuseppe Bonaccorso

Publisher: Packt Publishing Ltd

ISBN: 1785884514

Category: Computers

Page: 360

View: 670


Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

Dialogmarketing Perspektiven 2017/2018

Tagungsband 12. wissenschaftlicher interdisziplinärer Kongress für Dialogmarketing

Author: Deutscher Dialogmarketing

Publisher: Springer-Verlag

ISBN: 3658205989

Category: Business & Economics

Page: 229

View: 3374


Der Sammelband Dialogmarketing Perspektiven vereint aktuelle Fachbeiträge und Forschungsprojekte zu Themen des Dialogmarketings und erscheint 2017/2018 bereits im zwölften Jahr. Er geht zurück auf den 12. wissenschaftlichen interdisziplinären Kongress für Dialogmarketing, den der DDV im September 2017 an der Universität Kassel veranstaltete. Der Deutsche Dialogmarketing Verband hat es sich zur Aufgabe gemacht, den Austausch zwischen Wissenschaft und Praxis in dieser innovationsstarken Branche zu fördern.

Mastering Social Media Mining with R

Author: Sharan Kumar Ravindran,Vikram Garg

Publisher: Packt Publishing Ltd

ISBN: 1784399671

Category: Computers

Page: 248

View: 1219


Extract valuable data from your social media sites and make better business decisions using R About This Book Explore the social media APIs in R to capture data and tame it Employ the machine learning capabilities of R to gain optimal business value A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data Who This Book Is For If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful. What You Will Learn Access APIs of popular social media sites and extract data Perform sentiment analysis and identify trending topics Measure CTR performance for social media campaigns Implement exploratory data analysis and correlation analysis Build a logistic regression model to detect spam messages Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations Develop recommendation systems using Collaborative Filtering and the Apriori algorithm In Detail With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst. Style and approach This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.

Mastering Java for Data Science

Author: Alexey Grigorev

Publisher: N.A

ISBN: 9781782174271


Page: 393

View: 8860


Become an expert at building and deploying enterprise-grade data applications in JavaAbout This Book* This comprehensive book shows you exactly how you can take your Java data science applications to production seamlessly* Dive deep into analytics, supervised and unsupervised learning, and much more with ease* Explore Java's various libraries to efficiently build and deploy data applications for the enterpriseWho This Book Is ForThis book is for those Java developers who are comfortable with developing applications in Java and are familiar with the basic concepts of data science. This is the go-to book for anyone looking to master the subject using Java. If you are willing to build efficient data applications in your enterprise environment without changing your existing stack, this book is for you!What you will learn* Get a solid understanding of the data processing toolbox available in Java* Explore the data science ecosystem available in Java and other JVM languages* Understand when to use Java and what is best to do outside of Java* Deal with the machine learning task at hand and bring the results directly to production* Get state-of-the-art performance with xgboost and deeplearning4j* Build applications that scale and process large amounts of data in real timeIn DetailJava is the language of choice if you want to bring data science to production, thanks to its stability and rich set of libraries. Major big data solutions including Hadoop are written in Java. This book will teach you how to perform data analysis on big data in a much more sophisticated manner. If you are willing to take your data products to enterprise without changing your stack, this book will tell you how to do it with ease.This book will quickly brush up on what you already know about using Java in data science applications and will then dive quickly into the advanced concepts to implement data science in production. The book covers topics such as advanced data science algorithms, preparing tricky data, advanced clustering, regression, classification, prediction, machine learning, and more.We'll teach you how data science can be used effectively to analyze unstructured data and big data. This book will enable you to tackle the problems of advanced visualization, advanced statistics, scaling data science applications, deploying these applications in production, and many more. You will also learn about natural language processing, real-time analytics, deep learning, and neural networks.

Mastering Predictive Analytics with R

Author: Rui Miguel Forte

Publisher: Packt Publishing Ltd

ISBN: 1783982810

Category: Computers

Page: 414

View: 801


R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real world data sets. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics.

Die 4-Stunden-Woche

Mehr Zeit, mehr Geld, mehr Leben

Author: Timothy Ferriss

Publisher: Ullstein eBooks

ISBN: 3843704457

Category: Business & Economics

Page: 352

View: 770


Warum arbeiten wir uns eigentlich zu Tode? Haben wir nichts Besseres zu tun? Und ob! - sagt Timothy Ferriss. Der junge Unternehmer war lange Workaholic mit 80-Stunden-Woche. Doch dann erfand er MBA - Management by Absence - und ist seitdem freier, reicher, glücklicher. Mit viel Humor, provokanten Denkanstößen und erprobten Tipps erklärt Ferriss, wie sich die 4-Stunden-Woche bei vollem Lohnausgleich verwirklichen lässt. Der Wegweiser für eine Flucht aus dem Hamsterrad und ein Manifest für eine neue Gewichtung zwischen Leben und Arbeiten.

Data Science mit Python

Das Handbuch für den Einsatz von IPython, Jupyter, NumPy, Pandas, Matplotlib und Scikit-Learn

Author: Jake VanderPlas

Publisher: MITP-Verlags GmbH & Co. KG

ISBN: 3958456979

Category: Computers

Page: 552

View: 1506


Die wichtigsten Tools für die Datenanalyse und-bearbeitung im praktischen Einsatz Python effizient für datenintensive Berechnungen einsetzen mit IPython und Jupyter Laden, Speichern und Bearbeiten von Daten und numerischen Arrays mit NumPy und Pandas Visualisierung von Daten mit Matplotlib Python ist für viele die erste Wahl für Data Science, weil eine Vielzahl von Ressourcen und Bibliotheken zum Speichern, Bearbeiten und Auswerten von Daten verfügbar ist. In diesem Buch erläutert der Autor den Einsatz der wichtigsten Tools. Für Datenanalytiker und Wissenschaftler ist dieses umfassende Handbuch von unschätzbarem Wert für jede Art von Berechnung mit Python sowie bei der Erledigung alltäglicher Aufgaben. Dazu gehören das Bearbeiten, Umwandeln und Bereinigen von Daten, die Visualisierung verschiedener Datentypen und die Nutzung von Daten zum Erstellen von Statistiken oder Machine-Learning-Modellen. Dieses Handbuch erläutert die Verwendung der folgenden Tools: ● IPython und Jupyter für datenintensive Berechnungen ● NumPy und Pandas zum effizienten Speichern und Bearbeiten von Daten und Datenarrays in Python ● Matplotlib für vielfältige Möglichkeiten der Visualisierung von Daten ● Scikit-Learn zur effizienten und sauberen Implementierung der wichtigsten und am meisten verbreiteten Algorithmen des Machine Learnings Der Autor zeigt Ihnen, wie Sie die zum Betreiben von Data Science verfügbaren Pakete nutzen, um Daten effektiv zu speichern, zu handhaben und Einblick in diese Daten zu gewinnen. Grundlegende Kenntnisse in Python werden dabei vorausgesetzt. Leserstimme zum Buch: »Wenn Sie Data Science mit Python betreiben möchten, ist dieses Buch ein hervorragender Ausgangspunkt. Ich habe es sehr erfolgreich beim Unterrichten von Informatik- und Statistikstudenten eingesetzt. Jake geht weit über die Grundlagen der Open-Source-Tools hinaus und erläutert die grundlegenden Konzepte, Vorgehensweisen und Abstraktionen in klarer Sprache und mit verständlichen Erklärungen.« – Brian Granger, Physikprofessor, California Polytechnic State University, Mitbegründer des Jupyter-Projekts

Mastering Scientific Computing with R

Author: Paul Gerrard,Radia M. Johnson

Publisher: Packt Publishing Ltd

ISBN: 1783555262

Category: Computers

Page: 432

View: 7709


If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.

Statistik-Workshop für Programmierer

Author: Allen B. Downey

Publisher: O'Reilly Germany

ISBN: 3868993436

Category: Computers

Page: 160

View: 7022


Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

MATLAB for Machine Learning

Author: Giuseppe Ciaburro

Publisher: Packt Publishing Ltd

ISBN: 1788399390

Category: Computers

Page: 382

View: 1084


Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

Datenanalyse mit Python

Auswertung von Daten mit Pandas, NumPy und IPython

Author: Wes McKinney

Publisher: O'Reilly

ISBN: 3960102143

Category: Computers

Page: 542

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Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen.Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar.Aus dem Inhalt:Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative ComputingLernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennenSetzen Sie die Datenanalyse-Tools der pandasBibliothek einVerwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von DatenErstellen Sie interformative Visualisierungen mit matplotlibWenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassenAnalysieren und manipulieren Sie verschiedenste Zeitreihen-DatenFür diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.