{"product_id":"machine-learning-essentials-practical-guide-in-r-9781986406857","title":"Machine Learning Essentials: Practical Guide in R","description":"\u003cp\u003e • Author(s): Alboukadel Kassambara\u003cbr\u003e • Publisher: Createspace Independent Publishing Platform\u003cbr\u003e • Publisher Imprint: Createspace Independent Publishing Platform\u003cbr\u003e • BISAC: Data Science - General\u003c\/p\u003e\u003cp\u003eDiscovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques.\u003cbr\u003e\u003cbr\u003eThis book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models.\u003cbr\u003e\u003cbr\u003e The main parts of the book include: A) \u003cb\u003eUnsupervised learning methods\u003c\/b\u003e, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) \u003cb\u003eRegression analysis\u003c\/b\u003e, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) \u003cb\u003eClassification techniques\u003c\/b\u003e, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) \u003cb\u003eAdvanced machine learning methods\u003c\/b\u003e, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) \u003cb\u003eModel selection methods\u003c\/b\u003e, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) \u003cb\u003eModel validation and evaluation techniques\u003c\/b\u003e for measuring the performance of a predictive model. G) \u003cb\u003eModel diagnostics\u003c\/b\u003e for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers.\u003cbr\u003e\u003cbr\u003e Key features: \u003cbr\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eCovers machine learning algorithm and implementation\u003c\/li\u003e\n\u003cli\u003eKey mathematical concepts are presented\u003c\/li\u003e\n\u003cli\u003eShort, self-contained chapters with practical examples. \u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Createspace Independent Publishing Platform","offers":[{"title":"Paperback","offer_id":45533367500951,"sku":"9781986406857","price":4274.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781986406857.webp?v=1767111499","url":"https:\/\/atlanticbooks.com\/products\/machine-learning-essentials-practical-guide-in-r-9781986406857","provider":"Atlantic Books","version":"1.0","type":"link"}