Data Science Blog Index
Artificial Intelligence (AI) is on a trajectory to become ubiquitous in every area of our lives. In this post, we explore three current AI applications and the machine learning techniques that make them work.
In this blog post we talk about 5 aspects of machine learning that can be applied to transportation.
- Self-driving cars
- Congestion Prediction
- Infrastructure maintenance
- Predicting vehicle maintenance
- Public transport optimizations
Customer lifetime value goes a step further than churn rate prediction. It uses retention (i.e. the inverse of churn) and a “discount” factor, which accounts for the decreasing value of future money to predict the expected monetary value that a customer will generate over the entire period of the relationship with your company.
Client churn is the turnover (or attrition) rate over time of your company’s customers. Since keeping clients is often much more cost effective than finding new ones, by understanding who is likely to churn and why, we can create a cost-effective way to increase profits.
In this blog, we will cover a broad spectrum of topics in Finance that relate to predicting trends and outliers on both the small and large scales. We will cover
- Commercial Growth Predictions by Monitoring Parking Lots
- Stock Predictions with Weather Forecasts
- Anomaly Detection use to find Fraud
- Market Forecasts with Sentiment Analysis
We introduce the concept of Reinforcement Learning and discuss how such an algorithm can be used to solve portfolio optimizations that extract the most value from raw market data. Over time, such algorithms learn the strategies to deploy that will generate the maximum possible value and consequently the maximum possible profit.
In this post we’ll be discussing the potential avenues for using supervised learning in finance for stock market prediction.
The goal of text summarization is to automatically condense unstructured text into summaries, containing only the most important information.
Artificial Neural Networks were originally inspired by the human nervous system. The term has entered popular parlance in recent years due to the huge advancements in computational power and data storage abilities. We here give a brief summary of what Neural Nets are and how they work.
Document classification is currently one of the most important branches of Natural Language Processing (NLP). The general idea is to automatically classify documents into categories using machine learning algorithms.
The applications are almost endless, we can classify: patient records, movie reviews, webpages, emails (spam vs not spam), and in fact anything text based.
Relation Extraction is one of the cornerstones of Natural Language Processing and concerns the linking of two entities in unstructured text. In this blog post, we describe two of the simplest methods that are typically used: Supervised Feature Extraction and Semi-Supervised Seeding.
Dynamic Time Warping (DTW) is an intelligent, dynamically adjusted metric that allows more flexibility when used in combination with any distance dependent algorithm. This flexibility allows for better classification results in many different time-series analyses.
DTW allows us to retain the temporal dynamics by directly modeling the time-series. As its names suggests, the usual Euclidean distance in problems is replaced with a dynamically adjusted metric.
Machine vision is one of the pillars of machine learning. Its stature will grow in importance as more accurate algorithms are explored, but has already been used by Amazon for online shopping, Google for image search and Facebook for its facial recognition. Here we give a brief overview of convolutional neural networks, one of the best deep learning algorithms for image recognition.
The goal for this project was to determine from raw neural activity what the volunteer was thinking at any given time. This form of analysis have many direct applications, for example: prosthesis moving, computer games which interface with brain activity, and even piloting vehicles without need for physical interactions.
Natural language processing has been used in speech recognition, spell-checking, document classification, and more. Moreover, it's a stepping stone to developing strong AI, one which can intelligently parse information given to it better than a human.