Time series analysis is a very useful and powerful technique for studying data that changes over time, such as sales, traffic, climate, etc. Anomaly detection is the process of identifying values or events that deviate from the normal trend of the data. In this article, I will explain what a…

Artificial intelligence (AI) is one of the most innovative and revolutionary technologies of our time. However, for many companies, adopting AI can be a challenge, due to high costs, technical complexity, and talent scarcity. How can businesses reap the benefits of AI without having to deal with these obstacles? The…

1. What is autoregression and why is it useful Autogression is a statistical model that uses the past values of a variable to predict future values. It is based on the hypothesis that there is a relationship between the values observed at different points of time, called delays. Autoregression is useful for analyzing time series, which are sequences of…

1. What is conditional heteroskedasticity and why is it important for time series Conditional heteroskedasticity is a characteristic that occurs when the variability of a variable depends on the information available at the previous time. This means that the variance of a variable is not constant, but varies randomly and predictably. For example, if we look at the returns of a stock, we…

1. What is data loss and why is it a serious problem for the quality and reliability of machine learning models Data loss is a phenomenon that occurs when the data used to train or evaluate a machine learning model is incomplete, incorrect, or inconsistent. Data loss can have several causes, such as measurement errors, omissions, manipulations, transformations, or data selections. Furthermore, it can negatively affect the quality and reliability of…

1. Introduction to ranking and its application scenarios Ranking is a class of supervised learning algorithms that aim to sort a list of items based on their relevance to a query. Ranking has many applications, such as search engines and recommendation systems. Ranking is based on feature vectors that describe a query and a document, and a function…

Chunking’s Fundamental Role in LLM Content Integration Chunking is a technique that consists of splitting a text into smaller, more homogeneous units, called chunks, which can be easily processed by a language model (LLM). Chunking plays a key role in optimizing semantic responses, as it allows you to reduce the complexity of the text and increase the…

In this article, I want to share with you my passion for signal processing, a discipline that deals with studying and manipulating information that depends on one or more independent variables, such as time, space, frequency, etc. Signal processing is very important for data science and machine learning, because it…

Causal Inference is a very relevant subject for Data Science, as it allows us to go beyond the simple description of data and to understand what are the effects of a certain action or intervention on a variable of interest. This way, we can make more informed, evidence-based decisions. 1. What is Causal Inference and why is it important? Causal…