Download or read book R Recipes for Analysis Visualization and Machine Learning written by Viswa Viswanathan and published by Packt Publishing Ltd. This book was released on 2016-11-24 with total page 958 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning About This Book Proficiently analyze data and apply machine learning techniques Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R Construct a predictive model by using a variety of machine learning packages Who This Book Is For This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R – it will help you increase your R expertise, regardless of your level of experience. What You Will Learn Get data into your R environment and prepare it for analysis Perform exploratory data analyses and generate meaningful visualizations of the data Generate various plots in R using the basic R plotting techniques Create presentations and learn the basics of creating apps in R for your audience Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm Build, tune, and evaluate predictive models with different machine learning packages Incorporate R and Hadoop to solve machine learning problems on big data In Detail The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction. 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: R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan R Data Visualization Cookbook by Atmajitsinh Gohil Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu) Style and approach This course creates a smooth learning path that will teach you how to analyze data and create stunning visualizations. The step-by-step instructions provided for each recipe in this comprehensive Learning Path will show you how to create machine learning projects with R.

Download or read book Regression Analysis Recipes written by Geetha Subramanian and published by Apress. This book was released on 2022-10-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use regression analysis tools to solve problems in Python and R. This book provides problem-solving solutions in Python and R using familiar datasets such as Iris, Boston housing data, King County House dataset, etc. You'll start with an introduction to the various methods of regression analysis and techniques to perform exploratory data analysis. Next, you'll review problems and solutions on different regression techniques with building models for better prediction. The book also explains building basic models using linear regression, random forest, decision tree, and other regression methods. It concludes with revealing ways to evaluate the models, along with a brief introduction to plots. Each example will help you understand various concepts in data science. You'll develop code in Python and R to solve problems using regression methods such as linear regression, support vector regression, random forest regression. The book also provides steps to get details about Imputation methods, PCA, variance measures, CHI2, correlation, train and test models, outlier detection, feature importance, one hot encoding, etc. Upon completing Regression Analysis Recipes, you will understand regression analysis tools and techniques and solve problems in Python and R. What You'll Learn Perform regression analysis on data using Python and R Understand the different kinds of regression methods Use Python and R to perform exploratory data analysis such as outlier detection, imputation on different types of datasets Review the different libraries in Python and R utilized in regression analysis Who This Book Is For Software Professionals who have basic programming knowledge about Python and R

Download or read book R Cookbook written by Paul Teetor and published by "O'Reilly Media, Inc.". This book was released on 2011-03-03 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data "Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author

Download or read book R Cookbook written by JD Long and published by O'Reilly Media. This book was released on 2019-06-21 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this book will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform basic functions Simplify data input and output Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data

Download or read book R Cookbook written by JD Long and published by "O'Reilly Media, Inc.". This book was released on 2019-06-21 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this book will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform basic functions Simplify data input and output Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data

Download or read book 25 Recipes for Getting Started with R written by Paul Teetor and published by "O'Reilly Media, Inc.". This book was released on 2011-02-07 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a powerful tool for statistics and graphics, but getting started with this language can be frustrating. This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to use R for basic statistics, graphics, and regression. You'll find recipes on reading data files, creating data frames, computing basic statistics, testing means and correlations, creating a scatter plot, performing simple linear regression, and many more. These solutions were selected from O'Reilly's R Cookbook, which contains more than 200 recipes for R that you'll find useful once you move beyond the basics.

Download or read book Data Analysis Using Regression and Multilevel Hierarchical Models written by Andrew Gelman and published by Cambridge University Press. This book was released on 2007 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Download or read book Computer Algebra Recipes written by Richard H. Enns and published by Springer Science & Business Media. This book was released on 2007-02-26 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: * Contains computer algebra worksheets or "recipes" designed using MAPLE (System 10); no prior knowledge of MAPLE is assumed * Effective computational science text for first- and second-year undergraduates in mathematics, physics, engineering, chemistry, economics, biology, and pre-medicine * Examples and problems provide basis for both self-study and on-line course

Download or read book Python Data Cleaning Cookbook written by Michael Walker and published by Packt Publishing Ltd. This book was released on 2024-05-31 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the intricacies of data description, issue identification, and practical problem-solving, armed with essential techniques and expert tips. Key Features Get to grips with new techniques for data preprocessing and cleaning for machine learning and NLP models Use new and updated AI tools and techniques for data cleaning tasks Clean, monitor, and validate large data volumes to diagnose problems using cutting-edge methodologies including Machine learning and AI Book DescriptionJumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes. Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. You will learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Next, you’ll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify unexpected values. Finally, you’ll build functions and classes that you can reuse without modification when you have new data. By the end of this Data Cleaning book, you'll know how to clean data and diagnose problems within it.What you will learn Using OpenAI tools for various data cleaning tasks Producing summaries of the attributes of datasets, columns, and rows Anticipating data-cleaning issues when importing tabular data into pandas Applying validation techniques for imported tabular data Improving your productivity in pandas by using method chaining Recognizing and resolving common issues like dates and IDs Setting up indexes to streamline data issue identification Using data cleaning to prepare your data for ML and AI models Who this book is for This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data with practical examples. Working knowledge of Python programming is all you need to get the most out of the book.

Download or read book PySpark Recipes written by Raju Kumar Mishra and published by Apress. This book was released on 2017-12-09 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! PySpark Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will learn to apply RDD to solve day-to-day big data problems. Python and NumPy are included and make it easy for new learners of PySpark to understand and adopt the model. What You Will Learn Understand the advanced features of PySpark2 and SparkSQL Optimize your code Program SparkSQL with Python Use Spark Streaming and Spark MLlib with Python Perform graph analysis with GraphFrames Who This Book Is For Data analysts, Python programmers, big data enthusiasts

Download or read book Time Series Analysis with Python Cookbook written by Tarek A. Atwan and published by Packt Publishing Ltd. This book was released on 2022-06-30 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: Perform time series analysis and forecasting confidently with this Python code bank and reference manual Key Features • Explore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms • Learn different techniques for evaluating, diagnosing, and optimizing your models • Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities Book Description Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you'll work with ML and DL models using TensorFlow and PyTorch. Finally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book. What you will learn • Understand what makes time series data different from other data • Apply various imputation and interpolation strategies for missing data • Implement different models for univariate and multivariate time series • Use different deep learning libraries such as TensorFlow, Keras, and PyTorch • Plot interactive time series visualizations using hvPlot • Explore state-space models and the unobserved components model (UCM) • Detect anomalies using statistical and machine learning methods • Forecast complex time series with multiple seasonal patterns Who this book is for This book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.

Download or read book PyTorch Recipes written by Pradeepta Mishra and published by Apress. This book was released on 2019-01-28 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them. Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language processing and text processing using PyTorch. What You Will LearnMaster tensor operations for dynamic graph-based calculations using PyTorchCreate PyTorch transformations and graph computations for neural networksCarry out supervised and unsupervised learning using PyTorch Work with deep learning algorithms such as CNN and RNNBuild LSTM models in PyTorch Use PyTorch for text processing Who This Book Is For Readers wanting to dive straight into programming PyTorch.

Download or read book Microsoft Power BI Cookbook written by Greg Deckler and published by Packt Publishing Ltd. This book was released on 2021-09-30 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher's note: This edition from 2021 is outdated and does not cover the most recent Power BI updates. A new third edition, updated to the latest release is now available in color Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Update your knowledge with new recipes for query optimization, aggregation tables, Power BI API, and paginated reports Work with recipes across diverse Power BI platforms including the Power BI Service and Mobile Applications Implement custom solutions with M and DAX languages through actionable guidance and proven development techniques Book DescriptionThe complete everyday reference guide to Power BI, written by an internationally recognized Power BI expert duo, is back with a new and updated edition. Packed with revised practical recipes, Microsoft Power BI Cookbook, Second Edition, helps you navigate Power BI tools and advanced features. It also demonstrates the use of end-to-end solutions that integrate those features to get the most out of Power BI. With the help of the recipes in this book, you’ll gain advanced design and development insight, practical tips, and guidance on enhancing existing Power BI projects. The updated recipes will equip you with everything you need to know to implement evergreen frameworks that will stay relevant as Power BI updates. You’ll familiarize yourself with Power BI development tools and services by going deep into the data connectivity, transformation, modeling, visualization, and analytical capabilities of Power BI. By the end of this book, you’ll make the most of Power BI’s functional programming languages of DAX and M and deliver powerful solutions to common business intelligence challenges.What you will learn Cleanse, stage, and integrate your data sources with Power Query (M) Remove data complexities and provide users with intuitive, self-service BI capabilities Build business logic and analysis into your solutions via the DAX programming language and dashboard-ready calculations Implement aggregation tables to accelerate query performance over large data sources Create and integrate paginated reports Understand the differences and implications of DirectQuery, live connections, Import, and Composite model datasets Integrate other Microsoft data tools into your Power BI solution Who this book is for If you’re a BI professional who wants to up their knowledge of Power BI and offer more value to their organization, then this book is for you. Those looking for quick solutions to common Power BI problems will also find this book an extremely useful resource. Please be aware that this is not a beginner’s guide; you’ll need a solid understanding of Power BI and experience working with datasets before you dive in.

Download or read book Deep Learning with MXNet Cookbook written by Andrés P. Torres and published by Packt Publishing Ltd. This book was released on 2023-12-29 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to production Key Features Create scalable deep learning applications using MXNet products with step-by-step tutorials Implement tasks such as transfer learning, transformers, and more with the required speed and scalability Analyze model performance and fine-tune for accuracy, scalability, and speed Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionExplore the capabilities of the open-source deep learning framework MXNet to train and deploy neural network models and implement state-of-the-art (SOTA) architectures in Computer Vision, natural language processing, and more. The Deep Learning with MXNet Cookbook is your gateway to constructing fast and scalable deep learning solutions using Apache MXNet. Starting with the different versions of MXNet, this book helps you choose the optimal version for your use and install your library. You’ll work with MXNet/Gluon libraries to solve classification and regression problems and gain insights into their inner workings. Venturing further, you’ll use MXNet to analyze toy datasets in the areas of numerical regression, data classification, picture classification, and text classification. From building and training deep-learning neural network architectures from scratch to delving into advanced concepts such as transfer learning, this book covers it all. You'll master the construction and deployment of neural network architectures, including CNN, RNN, LSTMs, and Transformers, and integrate these models into your applications. By the end of this deep learning book, you’ll wield the MXNet and Gluon libraries to expertly create and train deep learning networks using GPUs and deploy them in different environments.What you will learn Grasp the advantages of MXNet and Gluon libraries Build and train network models from scratch using MXNet Apply transfer learning for more complex, fine-tuned network architectures Address modern Computer Vision and NLP problems using neural network techniques Train state-of-the-art models with GPUs and leverage modern optimization techniques Improve inference run-times and deploy models in production Who this book is for This book is for data scientists, machine learning engineers, and developers who want to work with Apache MXNet for building fast and scalable deep learning solutions. Python programming knowledge and access to a working coding environment with Python 3.6+ is necessary to get started. Although not a prerequisite, a solid theoretical understanding of mathematics for deep learning will be beneficial.

Download or read book R Recipes written by Larry Pace and published by Apress. This book was released on 2014-12-24 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: R Recipes is your handy problem-solution reference for learning and using the popular R programming language for statistics and other numerical analysis. Packed with hundreds of code and visual recipes, this book helps you to quickly learn the fundamentals and explore the frontiers of programming, analyzing and using R. R Recipes provides textual and visual recipes for easy and productive templates for use and re-use in your day-to-day R programming and data analysis practice. Whether you're in finance, cloud computing, big or small data analytics, or other applied computational and data science - R Recipes should be a staple for your code reference library.

Download or read book Extending Excel with Python and R written by Steven Sanderson and published by Packt Publishing Ltd. This book was released on 2024-04-30 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity Key Features Perform advanced data analysis and visualization techniques with R and Python on Excel data Use exploratory data analysis and pivot table analysis for deeper insights into your data Integrate R and Python code directly into Excel using VBA or API endpoints Purchase of the print or Kindle book includes a free PDF eBook Book Description– Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics. – This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. – Working through the chapters, you’ll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. – Both beginners and experts will get everything you need to unlock Excel's full potential and take your data analysis skills to the next level. – By the end of this book, you’ll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed. What you will learn Read and write Excel files with R and Python libraries Automate Excel tasks with R and Python scripts Use R and Python to execute Excel VBA macros Format Excel sheets using R and Python packages Create graphs with ggplot2 and Matplotlib in Excel Analyze Excel data with statistical methods and time series analysis Explore various methods to call R and Python functions from Excel Who this book is for – If you’re a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you. – The comprehensive approach to the topics covered makes it suitable for both beginners and intermediate learners. – A basic understanding of Excel, Python, and R is all you need to get started.

Download or read book A Recipe for Success Using SAS University Edition written by Sharon Jones and published by SAS Institute. This book was released on 2016-08-03 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Filled with helpful examples and real-life projects of SAS users, A Recipe for Success Using SAS University Edition is an easy guide on how to start applying the analytical power of SAS to real-world scenarios. This book shows you: how to start using analytics how to use SAS to accomplish a project goal how to effectively apply SAS to your community or school how users like you implemented SAS to solve their analytical problems A beginner’s guide on how to create and complete your first analytics project using SAS University Edition, this book is broken down into easy-to-read chapters that also include quick takeaway tips. It introduces you to the vocabulary and structure of the SAS language, shows you how to plan and execute a successful project, introduces you to basic statistics, and it walks you through case studies to inspire and motivate you to complete your own projects. Following a recipe for success using this book, harness the power of SAS to plan and complete your first analytics project!