Tattar Prabhanjan Narayanachar Tattar – författare
512 kr
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Software Implementation Illustrated with R and Python
About This Book
Learn the nature of data through software which takes the preliminary concepts right away using R and Python.Understand data modeling and visualization to perform efficient statistical analysis with this guide.Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.Who This Book Is For
If you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versa
What You Will Learn
Learn the nature of data through software with preliminary concepts right away in RRead data from various sources and export the R output to other softwarePerform effective data visualization with the nature of variables and rich alternative optionsDo exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inferenceLearn statistical inference through simulation combining the classical inference and modern computational powerDelve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statisticsIntroduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearityIn Detail
Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions.
This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world.
You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python.
The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics.
By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.
Style and approach
Developing better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning
428 kr
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Explore powerful R packages to create predictive models using ensemble methods
Key Features
Implement machine learning algorithms to build ensemble-efficient modelsExplore powerful R packages to create predictive models using ensemble methodsLearn to build ensemble models on large datasets using a practical approachBook Description
Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy.
Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you''ll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models.
By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.
What you will learn
Carry out an essential review of re-sampling methods, bootstrap, and jackknifeExplore the key ensemble methods: bagging, random forests, and boostingUse multiple algorithms to make strong predictive modelsEnjoy a comprehensive treatment of boosting methodsSupplement methods with statistical tests, such as ROCWalk through data structures in classification, regression, survival, and time series dataUse the supplied R code to implement ensemble methodsLearn stacking method to combine heterogeneous machine learning modelsWho this book is for
This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.