Statistical sampling is a fundamental block of statistics that allows us to obtain information on the population of interest efficiently, without studying it directly — One of the most hidden desires of any researcher is to be able to have data on the entire population that he / she intends to study. Studying the whole population allows the researcher to obtain a complete understanding of the phenomenon under study, since this allows to collect information…

An article exploring techniques for outlier detection in datasets. Learn how to use data visualization, z-scores, and clustering techniques to spot outliers in your dataset — Nassim Taleb writes how “tail” events define a large part of the success (or failure) of a phenomenon in the world. Everybody knows that you need more prevention than treatment, but few reward acts of prevention. N. Taleb — The Black Swan

How data scientists convert real-world objects in numerical representation for the development of machine learning models — Machine learning engineers leverage numerical representations of the world to build and train predictive algorithms. In the context of supervised learning, these representations allow the computer to learn the relationship between them and the target variable. Let’s imagine a vector to just be a list of numbers

Preparing data for modeling is one of the first most fundamental steps in the data science pipeline — Training predictive models requires that our data be in an appropriate format. We can’t feed our .csv file to a model and expect it to learn to generalize correctly. In this article, we will look at how to prepare data for machine learning, starting from the data preparation pipeline to…

We are experiencing a revolution. Artificial intelligence is (finally) making its way into our lives, creating a lot of amazement but also a lot of concern — We’re living a revolution. Artificial intelligence is (at last) making its way into our lives, creating a lot of amazement but also a lot of concern. Seeing how machine learning models become part of our everyday and working lives fills me with pride — being part of this world makes…

An introduction to PyTorch’s training loop and general approach to tackle the library’s steeper initial learning curve — In this post we will cover how to implement a logistic regression model using PyTorch in Python. PyTorch is one of the most famous and used deep learning frameworks by the community of data scientists and machine learning engineers in the world, and thus learning this tool becomes an essential…

In this article, I’ll explore another key concept in our battle against over/underfitting: the bias-variance trade-off. One of the main goals of machine learning is to build models that can generalize well on data that has not yet been seen. When building a model, it is important to ensure that…

Delving into L1 and L2 regularization techniques in Machine Learning to explain why they are important to prevent model overfitting — Machine Learning is a discipline that is experiencing enormous development in the technological and industrial fields. Thanks to its algorithms and modeling techniques, it is possible to build models capable of learning from past data, generalizing and making predictions on new data.

In this article we will explore Gensim, a very popular Python library for training text-based machine learning models, to train a Word2Vec model from scratch — Word2Vec is a machine learning algorithm that allows you to create vector representations of words. These representations, called embeddings, are used in many natural language processing tasks, such as word clustering, classification, and text generation.