Learn to expertly apply a range of machine learning methods to real
data with this practical guide.
Machine learning without advanced math! This book presents a serious,
practical look at machine learning, preparing you for valuable insights
on your own data. The Art of Machine Learning is packed with real
dataset examples and sophisticated advice on how to make full use of
powerful machine learning methods. Readers will need only an intuitive
grasp of charts, graphs, and the slope of a line, as well as familiarity
with the R programming language.
You'll become skilled in a range of machine learning methods, starting
with the simple k-Nearest Neighbors method (k-NN), then on to random
forests, gradient boosting, linear/logistic models, support vector
machines, the LASSO, and neural networks. Final chapters introduce text
and image classification, as well as time series. You'll learn not only
how to use machine learning methods, but also why these methods work,
providing the strong foundational background you'll need in practice.
Additional features:
How to avoid common problems, such as dealing with "dirty" data and
factor variables with large numbers of levels
A look at typical misconceptions, such as dealing with unbalanced data
Exploration of the famous Bias-Variance Tradeoff, central to machine
learning, and how it plays out in practice for each machine learning
method
Dozens of illustrative examples involving real datasets of varying size
and field of application
Standard R packages are used throughout, with a simple wrapper interface
to provide convenient access.
After finishing this book, you will be well equipped to start applying
machine learning techniques to your own datasets.
