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Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition de Brett Lantz

Descripción - Reseña del editor Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. What you will learn Discover the origins of machine learning and how exactly a computer learns by example Prepare your data for machine learning work with the R programming language Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks ? the basis of deep learning Avoid bias in machine learning models Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow Who this book is for Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.Table of Contents Introducing Machine Learning Managing and Understanding Data Lazy Learning – Classification Using Nearest Neighbors Probabilistic Learning – Classification Using Naive Bayes Divide and Conquer – Classification Using Decision Trees and Rules Forecasting Numeric Data – Regression Methods Black Box Methods – Neural Networks and Support Vector Machines Finding Patterns – Market Basket Analysis Using Association Rules Finding Groups of Data – Clustering with k-means Evaluating Model Performance Improving Model Performance Specialized Machine Learning Topics Biografía del autor Brett Lantz (@DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.

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Detalles del Libro

Name: Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
Autor: Brett Lantz
Categoria: Libros,Libros universitarios y de estudios superiores,Ciencias informáticas
Tamaño del archivo: 13 MB
Tipos de archivo: PDF Document
Idioma: Español
Archivos de estado: AVAILABLE

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Descargar Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play de David Foster libros ebooks, Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play Pdf descargar 📘 Lee Ahora 📥 Download Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play de David Foster Descripción - Reseña del editor Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to s...

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