Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning

By Mikaela Pisani • June 22, 2020 • 0 Comments

Whenever you want to analyze the performance of machine learning algorithms, you would need to study the root cause of the error. Concepts like bias and variance would help you understand this cause and give you insights on how to improve your model. What is Bias error? Bias error corresponds

MACHINE LEARNING – CHAPTER 1 – Introduction

By Mikaela Pisani • May 11, 2020 • 0 Comments

Machine learning is one of the hottest topics nowadays. People talk about machine learning as if it is magic. Organizations are racing to integrate machine learning into their functions. Everyone talks about it, but not too many people know what it really is. It is just math and statistics plus

Initial Coronavirus outbreak in France and South Korea

By Mikaela Pisani and Anthony Figueroa • April 27, 2020 • 0 Comments

We are currently experiencing the negative impacts of the novel Coronavirus. This virus has quickly changed our lives and has left many of us feeling confused and fearful of what’s to come. As engineers and data scientists – we want to help make sense of the overwhelming amount of data

Understanding Basic Statistics for Machine Learning Models – Part 3

By Mikaela Pisani • April 27, 2020 • 0 Comments

In this article you can find explanations for statistical concepts such as Statistical hypothesis test, used for answering questions about sample data and validating assumptions. In addition, it is provided a list of concepts regarding sampling distribution. Finally, we discuss the relationship between variance and bias. Statistical hypothesis testing States

Understanding Basic Statistics for Machine Learning Models – Part 2

By Mikaela Pisani • April 20, 2020 • 1 Comment

In this article, you will find basic information about distributions. It is expected that you have some knowledge about random variables and probability concepts such as variance, covariance, and expected value. You can find that information on Understanding Basic Statistics for Machine Learning Models – Part 1. What is a

Understanding Basic Statistics for Machine Learning Models – Part 1

By Mikaela Pisani • April 13, 2020 • 0 Comments

If you want to understand machine learning algorithms, it is very important to have basic statistical knowledge to understand what is behind them. Understanding how the algorithm operates gives you the possibility of configuring the model according to what you need, as well explaining with more confidence the results obtained

Skills For Data Scientists

By Mikaela Pisani • April 6, 2020 • 0 Comments

Being a data scientist requires a mix of skills that anyone can develop. You only need patience, time and be willing to undergo a process of trial and error. You need to understand businesses and be able to adapt to different situations according to the business’ needs. Another important skill

Recommendation Systems: How Suggestion Algorithms Work

By Anthony Figueroa • March 19, 2020 • 0 Comments

Oftentimes, we are surprised by the accuracy of recommendations on what to buy on Amazon, watch on Netflix, or listen on Spotify. We feel that somehow these companies know how our brain works and monetizing this magical guessing game. They have a deep foundation on behavioral sciences, and our job

A Quick Classification Experiment Using Machine Learning

By Bruno Michetti • December 27, 2019 • 1 Comment

My team recently faced a brand new challenge: developing a way to classify job positions written in natural language by lots of different people. It sounds simple, but there are a few factors that made this problem hard to solve. Job positions can be ambiguous depending on language usage and

From Summarization to Generalization and Prediction

By Anthony Figueroa • September 4, 2019 • 0 Comments

We can make predictions with machine learning by generalizing our data’s pertinent characteristics. Summarizing diverse datasets provides insight that can help produce more relevant generalizations.

Data predictions provide probabilities of future outcomes by mining and analyzing existing data, also called training data. Effective prediction is a mix of engineering, statistics, and intuition. Summarization can help by shaping this intuition. In the generalization phase, we test our training data against new data, called test data, to calculate if our model is good enough to be used in real life. These two processes simplify large multidimensional datasets, so machine learning predictions can be applied to them. This article describes how summarization leads to generalization and then prediction through a real estate example.