We can't find the internet
Attempting to reconnect
Something went wrong
Hang in there while we get back on track
£42.66
Same price for 6 weeks. Today is much like next week.
£43 for 43 days straight · last change was Sep 2026
Packt Publishing Machine Learning with R: Expert Techniques - 3rd Edition
We'll watch every seller, every day. One email when your price arrives.
Price History & Forecast
Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.
Last 90 days · 90 data points
NEW HERE?
Amazon shows you one price. We show you all of them.
Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.
WHAT'S ON THIS PAGE
when this has been cheap or pricey
where the price is heading next
all-time high & low, recent range
name your number, we'll email you
Price Distribution
Price distribution over 90 days • 4 price levels
Price Analysis
Most common price: £43 (56 days, 62.2%)
Price range: £36 - £44
Price levels: 4 different prices over 90 days
Description
Key Features
Third edition updated for R 3.6 and beyond with current best practices and modern machine learning approaches.
Teaches expert techniques for building flexible, effective machine learning models using R's comprehensive toolkit.
Features hands-on guidance from Brett Lantz, an experienced machine learning teacher and practitioner.
Covers real-world data transformation methods that convert raw information into actionable business knowledge.
Provides clear, practical examples throughout to help you quickly implement machine learning solutions.
Widely acclaimed bestseller trusted by thousands of R users for learning predictive modelling fundamentals.
Product Specifications
- Brand
- Packt Publishing
- Format
- paperback
- ASIN
- 1788295862
- Category
- Books > Subjects > Computing & Internet > Databases > Data Storage & Management > Data Mining
- Domain
- Amazon UK
- Release Date
- 15 April 2019
- Listed Since
- 22 March 2019
Barcode
No barcode data available
Similar Products You Might Like
Applied Machine Learning Using mlr3 in R
Chapman and Hall/CRC
Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems, 2nd Edition
Packt Publishing
Machine Learning with R
Springer
Machine Learning: Theory and Practice
CRC Press
CRC Press Hands-On Machine Learning with R Book
CRC Press
Machine Learning Using R: With Time Series and Industry-Based Use Cases in R
Apress
Mathematics and Programming for Machine Learning with R: From the Ground Up
CRC Press
Mathematics and Programming for Machine Learning with R: From the Ground Up
CRC Press
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R
Technics Publications
An Introduction to Machine Learning
Springer
Practical Business Analytics Using R and Python: Solve Business Problems Using a Data-driven Approach
Apress
Machine Learning
Springer
Machine Learning
Springer
Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining)
Wiley
A Greater Foundation for Machine Learning Engineering: The Hallmarks of the Great Beyond in Pytorch, R, Tensorflow, and Python
Authorhouse
Wiley Machine Learning for Business Analytics in R Textbook
Wiley
Machine Learning Refined: Foundations, Algorithms, and Applications
Cambridge University Press
Practical Machine Learning for Data Analysis Using Python
Academic Press
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining
Wiley
Machine Learning and Artificial Intelligence: Concepts, Algorithms and Models
Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner
Wiley
Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R
Packt Publishing
Python Machine Learning Case Studies: Five Case Studies for the Data Scientist
Apress
Statistical Learning from a Regression Perspective (Springer Texts in Statistics)
Springer