Название: Practical Machine Learning with R and Python: Machine Learning in Stereo, Third Edition Автор: Tinniam V Ganesh
Издательство: Amazon Digital Services LLC
ASIN: B07MP845XB
Год: 2018
Страниц: 442
Язык: английский
Формат: epub, pdf (conv)
Размер: 10.5 MB
This book implements many common Machine Learning algorithms in equivalent R and Python. This is the 3rd edition of the book. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques like best-fit, forward fit, backward fit, and lasso and ridge regression are also covered. The book further touches on classification metrics for computing accuracy, recall, precision etc. There are implementations of validation, ROC and AUC curves in both R and Python. Finally, the book covers unsupervised learning methods like K-Means, PCA and Hierarchical clustering. The book is well suited for the novice and the expert.
Plummeting hardware prices, more powerful processors and exploding data generation has led to the re-emergence of Artificial Intelligence (AI) and associated technologies. Datascience, Machine Learning (ML), Deep Learning (DL), Probabilistic Graphical Models, NLP have moved from research labs to our humble homes. In the last couple of years, the buzz around these three technologies namely Datascience, Machine Learning and Deep Learning has been getting louder and louder, that it is impossible for seasoned professionals or those beginning their careers to ignore these technologies.
The two most popular languages for Datascience, ML and DL are R and Python. There is a never-ending battle between diehard fans of these languages, about which language is better suited for datascience or machine learning. In my opinion, rather than debate about which is superior or otherwise, why should one not become adept in both. So, instead of implementing ML algorithms in one of the languages, this book implements equivalent ML algorithms in both languages.
The first two chapters discuss the most important programming constructs in R and Python. The third chapter highlights equivalent programming phrases in R and Python. Hence, those with no knowledge of R and Python will find these introductory chapters useful. Those who are proficient in one of the language can further their knowledge on the other. Those are familiar with both R and Python will find the equivalent implementations useful to internalize the algorithms. This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python.
Contents:
Preface
Introduction
1.Essential R
2.Essential Python for Datascience
3.R vs Python
4.Regression of a continuous variable
5.Classification and Cross Validation
6.Regression techniques and regularization
7.SVMs, Decision Trees and Validation curves
8.Splines, GAMs, Random Forests and Boosting
9.PCA, K-Means and Hierarchical Clustering
References