Series: Machine Learning - Part 1 – QUANT dira-t-on?

PCA

Understanding Principal Component Analysis

Logistic Regression

A dive into Logistic Regression.

Regularized Regressions

Linear models obtained with minimizing the SSR (Sum of Square Residuals) are great and easy to grasp. However, rarely all conditions are met and/or as the number of…

Gradient Descent

Gradient Descent is an optimization technique used in many Machine Learning algorithms to find the minimum of a function. It does require a convex and differentiable…

Linear Regression

A dive into the math behind the linear regression algorithm.

KNN - K Nearest Neighbor

Using KNN in both python and R

Defining Success

When evaluating models for a given ML algorithm, we need to define in advance what would be our metric to measure success. How would we decide if this model performs well or…

Naive-Bayes - Part 1

Making Naive-Bayes work in R

Intro to Kmeans

The purpose of this article is to get a deeper dive into Kmeans as an unsupervised machine learning algorithms. To see the algorithm at work on some financial data, you can…

Kmeans with regime changes

This post is about how to use Kmeans to classify various market regimes or to use Kmeans to classify financial observations.