Link to Content:

Created/Published/Taught by:

Peter Flach

Content Found Via:

Amazon

Free? No

Cost Range:

$35.49 - $64.00

Tags: bagging / boosting / classification / decision trees / distance-based models / feature construction / feature selection / linear models / machine learning / naive bayes / naive classification / probabilistic models / probability / random forests

Difficulty Rating:

from Amazon:

“As one of the most comprehensive machine learning texts around, this book does justice to the field’s incredible richness, but without losing sight of the unifying principles. Peter Flach’s clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.”

Go to Content: Machine Learning: The Art and Science of Algorithms that Make Sense of Data

## By magsol October 21, 2015 - 7:26 pm

This is a fantastic resource for beginners to gain an understanding of the inner workings of machine learning and statistics. It assumes very little a priori knowledge and works from a very intuitive standpoint: there are almost no formal proofs, and even equations are much more infrequent than you would see in other machine learning textbooks (e.g. Chris Bishop, Trevor Hastie et al, etc). In lieu of myriad formal proofs, the author uses toy examples throughout the book, often retooling the same example over multiple chapters to give the readers a familiar hook; for instance, the example of spam identification in email is used throughout to illustrate the various algorithms.

There is pseudocode interleaved throughout the text, giving a concrete example of how each algorithm would be implemented. However this text is very programming-agnostic, preferring to give the reader details and intuition for the algorithms as opposed to implementation details.

There are plenty of topics in machine learning that aren’t covered, but Peter Flach does a phenomenal job taking some of the highest elements of the field of machine learning and presenting them intuitively for beginners. Links to resources for deeper dives into the material of each chapter are provided at the end, giving the reader ample opportunity to fall down the rabbit hole of any of the topics covered.

Advanced Beginner- familiar with data science / new to this topic / some prerequisitesAdvanced - had practical experience with this topic beyond intermediate level / could teach others this or related topics

(2) A Little

Detailed Content Survey:Wanted to learn just for fun / Looked interesting

This was my first experience with this publisher/series

Yes

Most (i.e. read the entire book but didn't complete the exercises)

Some basic knowledge of linear algebra and multivariate calculus, and a general understanding of the context for machine learning.

None

Easy to understand

Beginners