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R: Complete Machine Learning Solutions
Use over 100 solutions to analyze data and build predictive models
About This BookVisualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithmCompare the differences between different regression methods to discover how they solve problemsIncorporate R and Hadoop to solve machine learning problems on big dataHarness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data scienceWho This Book Is ForAre you interested in understanding machine learning concepts and building real-time machine learning projects with R, but don’t know where to start? Then, this is the perfect course for you!
What You Will LearnCreate and inspect the transaction dataset and perform association analysis with the Apriori algorithmPredict possible churn users with the classification approachImplement the clustering method to segment customer dataCompress images with the dimension reduction methodBuild a product recommendation systemIn DetailThis course will take you from the very basics of R to creating insightful machine learning models with R.
We start off with basic R operations, reading data into R, manipulating data, forming simple statistics for visualizing data. We will then walk through the processes of transforming, analyzing, and visualizing the RMS Titanic data. You will also learn how to perform descriptive statistics.
This course will teach you to use regression models. We will then see how to fit data in tree-based classifier, Naive Bayes classifier, and so on.
We then move on to introducing powerful classification networks, neural networks, and support vector machines. During this journey, we will introduce the power of ensemble learners to produce better classification and regression results.
We will see how to apply the clustering technique to segment customers and further compare differences between each clustering method.
We will discover associated terms and underline frequent patterns from transaction data.
We will go through the process of compressing and restoring images, using the dimension reduction approach and R Hadoop, starting from setting up the environment to actual big data processing and machine learning on big data.
By the end of this course, we will build our own project in the e-commerce domain. Then, we will tackle the problem of personalization.
Style and approachThis course is full of hands-on recipes for machine learning with R. Each topic is fully explained, followed by step-by-step and practical examples.
This course is a blend of text, videos, code examples, and assessments, all packaged up keeping your journey in mind. The curator of this course has combined some of the best that Packt has to offer in one complete package. It includes content from the following Packt
R Machine Learning Solutions by Yu-Wei, Chiu (David Chiu)Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)R Machine Learning By Example by Raghav Bali and Dipanjan Sarkar
About This BookVisualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithmCompare the differences between different regression methods to discover how they solve problemsIncorporate R and Hadoop to solve machine learning problems on big dataHarness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data scienceWho This Book Is ForAre you interested in understanding machine learning concepts and building real-time machine learning projects with R, but don’t know where to start? Then, this is the perfect course for you!
What You Will LearnCreate and inspect the transaction dataset and perform association analysis with the Apriori algorithmPredict possible churn users with the classification approachImplement the clustering method to segment customer dataCompress images with the dimension reduction methodBuild a product recommendation systemIn DetailThis course will take you from the very basics of R to creating insightful machine learning models with R.
We start off with basic R operations, reading data into R, manipulating data, forming simple statistics for visualizing data. We will then walk through the processes of transforming, analyzing, and visualizing the RMS Titanic data. You will also learn how to perform descriptive statistics.
This course will teach you to use regression models. We will then see how to fit data in tree-based classifier, Naive Bayes classifier, and so on.
We then move on to introducing powerful classification networks, neural networks, and support vector machines. During this journey, we will introduce the power of ensemble learners to produce better classification and regression results.
We will see how to apply the clustering technique to segment customers and further compare differences between each clustering method.
We will discover associated terms and underline frequent patterns from transaction data.
We will go through the process of compressing and restoring images, using the dimension reduction approach and R Hadoop, starting from setting up the environment to actual big data processing and machine learning on big data.
By the end of this course, we will build our own project in the e-commerce domain. Then, we will tackle the problem of personalization.
Style and approachThis course is full of hands-on recipes for machine learning with R. Each topic is fully explained, followed by step-by-step and practical examples.
This course is a blend of text, videos, code examples, and assessments, all packaged up keeping your journey in mind. The curator of this course has combined some of the best that Packt has to offer in one complete package. It includes content from the following Packt
R Machine Learning Solutions by Yu-Wei, Chiu (David Chiu)Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)R Machine Learning By Example by Raghav Bali and Dipanjan Sarkar
160 pages, Kindle Edition
Published February 28, 2017
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