In previous articles, we’ve introduced how to use XGBoost to forecast future time series data points. We did that with univariate data and also added additional or exogenous variables. However, one limitation is that XGBoost models are not capable of handling multivariate data. Before moving forward, let’s clarify what each Read more…
Content-based recommender systems have emerged as a pivotal tool in the digital age, where the vastness of online content can often be overwhelming. These systems utilize various algorithms to analyze and filter information, providing personalized recommendations to users based on their individual preferences and behavior. By doing so, they help Read more…
You have your model trained and the endpoint created, but now you need to be able to use it. How can you do that? Let’s see how to use AWS Lambda for that! But first, what is AWS Lambda? It is a serverless computing service offered by Amazon Web Services Read more…
We will use a Kaggle dataset about credit card fraud detection. This dataset consists of transactions made by credit cards in September 2013 by European cardholders. It contains 492 frauds out of a total of 284,807 transactions. You can see that it is highly unbalanced, where the positive class (frauds) Read more…
In the last article, we introduced the theory behind Recurrent Neural Networks. This time we will use a simple example to illustrate the process of training a vanilla or basic RNNs to forecast time series data. We will import the basic libraries that we use in every single Data Science Read more…
Are you interested in mastering Time Series forecasting or natural language processing? Then you should learn about Recurrent Neural Networks. Recurrent Neural Networks or RNNs are a specialized form of neural network architecture engineered for sequence-based tasks. Unlike traditional feed-forward neural networks, which treat each input as independent, RNNs excel Read more…
In the last article, we learned how to train a Machine Learning model like Linear Regression or XGBoost to forecast Time Series data. We had to reframe the dataframe as a supervised learning problem. You can read about this process here. To explain the process we used Forex data, specifically Read more…
In a previous article, we talked about the Advanced Time Series models. We mentioned that these are models based on Machine Learning. Some of the most common ones are based on Neural Networks, these are: These are great examples, but before moving to explain them, we should have a look Read more…
Large Language Models or LLMs are Machine Learning models that use deep learning algorithms to process and understand natural language. These models are trained on extensive text data to find language patterns and relationships, enabling them to execute tasks like translation, sentiment analysis, and chatbot interactions. They are able to Read more…
So far we have been talking about classical approaches when forecasting time series data. However, it is essential to explore alternative techniques that involve advanced methodologies such as machine learning and deep learning. There are mixed views regarding the accuracy of these last techniques. Some say that these advanced techniques Read more…