An implementation with Python — Imagine you have a lot of structured data and you want to extract relevant information from them. You might also want to generate some simulations, asking for specific KPIs but also generating reports about the main relevances caught in data. In fact, with so much structured data, you might also…

From code to natural language — Model interpretability refers to the ease with which a human can understand the reasoning behind the predictions made by a machine learning model. It is the ability to understand how a model is making decisions and what factors are contributing to its predictions. There are many reasons why model interpretability…

Overview of language models and hyperparameters — OpenAI and its disruptive development of generative models are probably the most trending topic in the Tech industry today. With good reason. The company indeed aims to promote and develop friendly AI in a way that benefits humanity as a whole. …

Part 1: Pre-built features — Azure Cognitive Services is a collection of cloud-based APIs that allow developers to add intelligent features to their applications. …

Part 10: Predictions — We got to the final Part of this series of articles, where I will use ML models to classify Judicial Sentences into categories. For this purpose, I will perform a supervised learning task using as a training set the already-labeled records, in order to apply the models to those records…

Part 9: Unsupervised Sentiment Analysis — Sentiment analysis is a natural language processing technique that involves using machine learning algorithms to identify and extract subjective information from text data. It is commonly used to determine the overall sentiment of a piece of writing, whether it is positive, negative, or neutral. This can be useful for a…

Part 8: Named Entities and Network representation — As we saw in previous articles, NLP deals with a vast amount of text data containing a huge amount of information. We saw many techniques to reduce complexity and extract relevant information from data, from the simplest tokenization/lemmatization/stemming to TF-IDF analysis and Words/Docs embedding. Another important aspect of analyzing these…

Part 7: Cluster analysis — In this article, I’m going through some further analysis and visual representation of our Judicial sentences. I will focus on clustering analysis, an unsupervised learning technique that is very useful to find patterns in data even if we do not know much about it. …

Data Labeling and AutoML — Azure Machine Learning (AML) is a cloud service for accelerating and managing the machine learning project lifecycle. So once the task is defined, it allows users to: Explore and prepare your dataset Develop, train and validate your model Deploy models Monitor and manage models and dataset lifecycle with MLops In…