## Machine Learning Algorithms – Second Edition

The second edition (fully revised, extended, and updated) of Machine Learning Algorithms has been published today and will be soon available through all channels. From the back cover: Machine learning has gained tremendous popularity for its powerful and fast predictions through large datasets. However, the true forces behind its powerful…

## Mastering Machine Learning Algorithms

Today I’ve published my latest book “Mastering Machine Learning Algorithms” (in a few days it will be available on all channels). From the back cover: Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides…

## Fundamentals of Machine Learning with Scikit-Learn

A tutorial video (2 hours) derived from the book Machine Learning Algorithms has been released: Fundamental of Machine Learning with Scikit-Learn: From the notes: As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for…

## ML Algorithms addendum: Passive Aggressive Algorithms

Passive Aggressive Algorithms are a family of online learning algorithms (for both classification and regression) proposed by Crammer at al. The idea is very simple and their performance has been proofed to be superior to many other alternative methods like Online Perceptron and MIRA (see the original paper in the…

## Linearly Separable? No? For me it is! A Brief introduction to Kernel Methods

This is a crash-introduction to kernel methods and the best thing to do is starting with a very simple question? Is this bidimensional set linearly separable? Of course, the answer is yes, it is. Why? A dataset defined in a subspace Ω ⊆ ℜn is linearly separable if there exists a (n-1)-dimensional hypersurface…

## PCA with Rubner-Tavan Networks

One of the most interesting effects of PCA (Principal Component Analysis) is to decorrelate the input covariance matrix C, by computing the eigenvectors and operating a base change using a matrix V:   The eigenvectors are sorted in descending order considering the corresponding eigenvalue, therefore Cpca is a diagonal matrix…

## A model-free collaborative recommendation system in 20 lines of Python code

Model-free collaborative filtering is a “lightweight” approach to recommendation systems. It’s always based on the implicit “collaboration” (in terms of ratings) among users, but it is computed in-memory without the usage of complex algorithms like ALS (Alternating Least Squares) that can be executed in parallel environment (like Spark). If we assume…