Packt , Learning
|Published:||Oct 25 2013|
|Posted:||Nov 19 2014|
|Book size:||5.39 MB|
Learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications Overview Harness the power of R for statistical computing and data science Use R to apply common machine learning algorithms with real-world applications Prepare, examine, and visualize data for analysis Understand how to choose between machine learning models Packed with clear instructions to explore, forecast, and classify data In Detail Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of Ra cross-platform, zero-cost statistical programming environmentthere has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data. What you will learn from this book Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches Use R to prepare data for machine learning Explore and visualize data with R Classify data using nearest neighbor methods Learn about Bayesian methods for classifying data Predict values using decision trees, rules, and support vector machines Forecast numeric values using linear regression Model data using neural networks Find patterns in data using association rules for market basket analysis Group data into clusters for segmentation Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, and big data Approach Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.
Apply effective learning algorithms to real-world problems using scikit-learn About This BookDesign and troubleshoot machine learning systems for common tasks including regression, classification, and clusteringAcquaint yourself with popular machine learning algorithms, including decision trees, logistic regression, and support vector machinesA practical example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learnWho This Book Is ForIf you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. In Detail This book exam...
Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data About This Book * Unlock and discover how to tackle clusters of raw data through practical examples in R * Explore your data and create your own models from scratch * Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guide Who This Book Is For This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement. What You Will ...
Build and Deploy Actionable Solutions in Minutes
Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented...
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