In this 10th anniversary edition, updated with 50% new content for R 4.0.0 and beyond, Brett Lantz looks at what's needed for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. It features several new chapters that reflect the progress of machine learning in the last few years to help readers build their data science skills and tackle more challenging problems, including making successful machine learning models, advanced data preparation and making use of big data.
Author: Brett Lantz Publisher: Packt Publishing Date: May 2023 Pages: 762 ISBN: 978-1801071321 Print: 1801071322 Kindle: B0B9YNZD15 Audience: R developers Level: Intermediate Category: Artificial Intelligence
Topics covered:
- Learn the end-to-end process of machine learning from raw data to implementation
- 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
- Prepare, transform, and clean data using the tidyverse
- Evaluate your models and improve their performance
- Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow
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Grokking Machine Learning
Author: Luis G. Serrano Publisher: Manning Date: December 2021 Pages: 512 ISBN: 978-1617295911 Print: 1617295914 Kindle: B09LK7KBSL Audience: Python developers interested in machine learning Rating: 5 Reviewer: Mike James Another book on machine learning - surely we have enough by now?
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Reliable Source: Lessons from a Life in Software Engineering
Author: James Bonang Date: January 2022 Pages: 608 Kindle: B09QCBVJ9V Audience: General interest Rating: 5 Reviewer: Kay Ewbank
This book combines a fun read with interesting insights into how to write reliable programs.
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