Com a popularização dos cartões de crédito nas ultimas duas décadas, trazendo a praticidade e conveniência para o dia-a-dia dos usuários, o cartão se tornou indispensável nos dias de hoje. Desde as compras mais simples no super mercado, consultas medicas, até transporte.De acordo com levantamento da Kantar, 65% das famílias brasileiras utilizam o cartão de crédito no dia-a-dia. … [Read more...] about Machine Learning na Detecção de Fraudes
Machine Learning
Fake News Detection: Text Pre-Processing
photo by Nathan Dumlao | UnsplashWith the explosion of online fake news and disinformation, it is increasingly difficult to discern fact from fiction. And as machine learning and natural language processing become more popular, Fake News detection serves as a great introduction to NLP.Google Cloud Natural Language API is a great platform to use for this project. Simply upload a … [Read more...] about Fake News Detection: Text Pre-Processing
How to encode Time-Series into Images for Financial Forecasting using Convolutional Neural Networks
Within forecasting there’s an age old question, ‘is what I am looking at a trend?’ Within the realm of statistics there are many tools that, with various degrees of success, answer said question. Yet, no approach has been able to achieve that which started the field of data forecasting in the first place. Looking at a graph derived from the data and drawing conclusions from … [Read more...] about How to encode Time-Series into Images for Financial Forecasting using Convolutional Neural Networks
Mental Emotional Sentiment Classification Using Machine Learning and an Electroencephalogram (EEG)
A couple of days ago, I was trying to teach myself how to use AI to understand and classify brainwaves when I came across this very interesting study by Jordan J. Bird.I have attached the official study, and an excellent youtube video he created at the end, as additional resources at the end of this article.You can also find the resources linked here:Study: … [Read more...] about Mental Emotional Sentiment Classification Using Machine Learning and an Electroencephalogram (EEG)
Evaluating Classification Model Performance
Accuracy is probably one of the easiest evaluation metrics to understand but is also often the least relevant. Accuracy measures the total number of correct predictions a classification model makes, including both true positives and true negatives. You can use the formula below to calculate:accuracy formulaUsing our COVID test example, this would be the percentage of total … [Read more...] about Evaluating Classification Model Performance
How DeepMind Tries to Recreate Relational Reasoning in Deep Neural Networks
Detecting relationships between objects remains a tremendous challenge in modern computer vision.Source: http://www.smartandsnazzykids.com/which-is-better-for-your-child-playing-alone-or-playing-with-others/I recently started an AI-focused educational newsletter, that already has over 70,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented … [Read more...] about How DeepMind Tries to Recreate Relational Reasoning in Deep Neural Networks
Text data representation with one-hot encoding, Tf-Idf, Count Vectors, Co-occurrence Vectors and…
This article is a comprehensive overview of some numerical representations of text data for Machine Learning algorithms.By Amador Loureiro from UnsplashBuilding machine learning models is not only restricted to numbers, we might want to be able to work with text as well. However, those models can only be fed with numbers. To bridge this gap a lot of research has gone into … [Read more...] about Text data representation with one-hot encoding, Tf-Idf, Count Vectors, Co-occurrence Vectors and…
Handling Data Scarcity while building Machine Learning applications
Fig. 1 Model growth analogy: from a seedling to a healthy plant (Image credits: Pixy)Data scarcity is when a) there is limited amount or a complete lack of labeled training data, or b) lack of data for a given label compared to the other labels (a.k.a data imbalance). Larger technology companies tend to have access to abundant data although they could encounter data imbalance. … [Read more...] about Handling Data Scarcity while building Machine Learning applications
Why Your Data Science Project Will Fail ?
By Emily Morter on UnsplashIf you are starting your life as a data scientist, you will hear around you things like why we didn’t get the expected performance? This model is a failure! We must find a solution to improve this model or We must start from the beginning.If you want to avoid to be in this kind of situation, you must avoid some beginner mistakes. Here, we will discuss … [Read more...] about Why Your Data Science Project Will Fail ?
Reinforcement Learning and Deep Reinforcement Learning
Reinforcement learning (RL) is an area of machine learning associated with agents willing to take actions in an environment in order to maximize the notion of a collective end reward. Games and the concept behind them have always had fragments of reinforcement learning and in ways, gaming domains are testing grounds for complex reinforcement algorithms.So what is the … [Read more...] about Reinforcement Learning and Deep Reinforcement Learning