Machine Learning
Regression, Classification, Ensembles and Evaluation
About this guide
A concise exam companion for machine learning. It covers supervised and unsupervised learning, linear and logistic regression, trees, support vector machines and ensembles, the confusion matrix and metrics from precision and recall to ROC-AUC, the bias-variance trade-off, regularisation and cross-validation, clustering and principal component analysis. Every chapter ends with a worked example carried through step by step, so you can check the reasoning yourself.
Inside the guide
- Unsupervised learning
- Linear and logistic regression
- Trees
- Support vector machines and ensembles
- Confusion matrix and metrics from precision and recall to ROC-AUC
- Bias-variance trade-off
- Regularisation and cross-validation
- Clustering and principal component analysis
Questions students ask
What does Machine Learning cover?
It covers supervised and unsupervised learning, linear and logistic regression, trees, support vector machines and ensembles, the confusion matrix and metrics from precision and recall to ROC-AUC, the bias-variance trade-off, regularisation and cross-validation, clustering and principal component analysis.
Who is Machine Learning for?
It is written for university students revising machine learning, and for lecturers and librarians choosing a clear, exam-focused reading-list text.
What format does it come in?
Kindle ebook and paperback on Amazon.
Can I read part of it for free?
Full Marks Press offers a free sample chapter at fullmarkspress.com/free, and the full guide is free to read with Kindle Unlimited.