Multilayer Perceptron is a Neural Network that learns the relationship between linear and non-linear data — This is the first article in a series dedicated to Deep Learning, a group of Machine Learning methods that has its roots dating back to the 1940’s. Deep Learning gained attention in the last decades for its groundbreaking application in areas like image classification, speech recognition, and machine translation.

Gradient Boosting algorithms tackle one of the biggest problems in Machine Learning: bias. — This is the third and last article in a series dedicated to Tree Based Algorithms, a group of widely used Supervised Machine Learning Algorithms. The first article was about Decision Trees, while the second explored Random Forests. Everything explained with real-life examples and some Python code. Gradient Boosting algorithms tackle…

Random Forests is a Machine Learning algorithm that tackles one of the biggest problems with Decision Trees: variance. — This is article number two in a series dedicated to Tree Based Algorithms, a group of widely used Supervised Machine Learning Algorithms. The first article was about Decision Trees. The next, and last article in this series, explores Gradient Boosted Decision Trees. …

Markov defined a way to represent real-world stochastic systems and processes that encode dependencies and reach a steady-state over time. — Andrei Markov didn’t agree with Pavel Nekrasov, when he said independence between variables was necessary for the Weak Law of Large Numbers to be applied. The Weak Law of Large Numbers states something like this: When you collect independent samples, as the number of samples gets bigger, the mean of…

Decision Tree is a Supervised Machine Learning Algorithm that uses a set of rules to make decisions, similarly to how humans make decisions. — This is article number one in a series dedicated to Tree Based Algorithms, a group of widely used Supervised Machine Learning Algorithms. Stay tuned if you’d like to see Decision Trees, Random Forests and Gradient Boosting Decision Trees, explained with real-life examples and some Python code.

Predicting your pizza’s cooking time — Gradient Descent is one of the most popular methods to pick the model that best fits the training data. Typically, that’s the model that minimizes the loss function, for example, minimizing the Residual Sum of Squares in Linear Regression. Stochastic Gradient Descent is a stochastic, as in probabilistic, spin on…

Ever since I can remember, I’ve been asked long-game questions without realizing it. I couldn’t identify them as long-game questions, because no one teaches you how to develop a long-game mindset. It’s not something you learn in school, discuss at the dinner table or at a barbecue with friends. The…

Several phenomena in the real world can be represented as counts of things. For example, the number of flights departing from an airport, number customers lining up at the store register, the number of earthquakes occurring in a year at a specific region. Counting events is a relatively simple task…

Logistic regression is a machine learning classification model with quite a confusing name! The name makes you think about Linear Regression, but it’s not used to predict an unbounded, continuous outcome. Instead, it is a statistical classification model, it gives you the likelihood that an observation belongs to a specific…