Machine Learning with R, the tidyverse, and mlr


Machine Learning with R, tidyverse, and mlr
By 作者: Hefin Ioan Rhys
Pages 页数: 398 pages
Edition 版本: 1
Language 语言: English
Publisher Finelybook 出版社: Manning Publications
Publication Date 出版日期: 2020-03-17
ISBN-10 书号:1617296570
ISBN-13 书号:9781617296574
The Book Description robot was collected from Amazon and arranged by Finelybook

Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer. Machine Learning with R, the tidyverse, and mlr teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools. This book will get you started!
Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you’ll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation.
What’s inside

Using the tidyverse packages to process and plot your data
Techniques for supervised and unsupervised learning
Classification, regression, dimension reduction, and clustering algorithms
Statistics primer to fill gaps in your knowledge

Brief
Contents


1Introduction to machine learning
2 Tidying, manipulating and plotting data with the tidyverse
3Classifying based on similar observations: the k-Nearest neighbors algorithm
4Classifying based on odds: logistic regression
5Classifying by maximizing class separation: discriminant analysis
6Classifying based on probabilities and hyperplanes: naive Bayes and support vector machines
7 Classifying with trees: decision trees
8Improving decision trees: random forests and gradient boosting
9Regression with lines: linear regression
10When the relationships aren't linear. generalized additive models
11 Preventing overfhtting: ridge regression, LASSO, and elastic net
12Regression with distance and trees:k-nearest neighbors, random forest and XGBoost
13 Maximizing variance: principal component analysis
14Maximizing similarity:t-SNE and UMAP
15 Dimension reduction with networks and local structure: self-organizing maps and locally-linear embedding
16Clustering by finding centers:k-means
17Clustering by finding hierarchies: hierarchical clustering
18Clustering based on density: DBSCAN and OPTICS
19 Clustering based on the distribution of data: mixture model clustering
20 Final notes and further reading
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