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. Worked examples are marked the way an examiner marks them, so you can see why an answer earns the grade.
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 get a free copy?
Yes. Full Marks Press offers free review copies to students and educators at fullmarkspress.com/free.