Recommender systems are a category of Machine Learning algorithms that predict a user's rating or preferences over a collection of items.  Recommender systems algorithms include: Collaborative filteringContent-based filteringSession-basedSession-based recommender systems  Reinforcement learning for recommender systems include: Multi-criteria recommender systemsRisk-aware recommender systemsMobile recommender systems 

In the reinforcement learning paradigm, the learning process is a loop in which the agent reads the state of the environment and then executes an action. Then the environment returns its new state and a reward signal, indicating if the action was correct or not. The process continues until the environment reaches a terminal condition … Continue reading Types of Machine Learning: Reinforcement Learning →

Given a set of documents, a common task is to group them accordingly to topics or subjects. A human agent can create a hierarchy of subjects and assign each document to its related issue. However, a clustering algorithm can create this structure automatically and more precisely. We can apply hierarchical clustering algorithms to group documents … Continue reading Machine Learning Applications: Topic Discovery with Clustering →

Exploratory data analysis is the most challenging task when building a machine learning model, especially for beginners. A result of the No-Free-Lunch-Theorem is that there's no single model that will perform well for every dataset. In other words, there's no silver bullet Machine Learning Algorithm. The practical consequence is that we need to make a … Continue reading Exploratory Data Analysis for Machine Learning →

The Stroke Prediction Dataset at Kaggle is an example of how to use Machine Learning for disease prediction. The dataset comprises more than 5,000 observations of 12 attributes representing patients' clinical conditions like heart disease, hypertension, glucose, smoking, etc. For each instance, there's also a binary target variable indicating if a patient had a stroke. We can build a … Continue reading Machine Learning Applications: Stroke Prediction →

An increasing application of machine learning classification algorithms is medical diagnose. Diagnosing if a patient has a specific disease is a simple binary classification problem. For example, we may build a model to identify if a patient has Hepatitis C. In this case, there are two possible outputs: yes and no, which is a typical … Continue reading Machine Learning Applications: Medical Diagnosis →

Spam detection is one of the classical applications of classification algorithms. It simply consists of assigning a received email one of two labels: spam or not spam. By automatically classifying received emails as spam or not spam, email services provide a cleaner and safer mail Inbox. The training data is obtained by collecting samples of … Continue reading Classification Applications: Spam Detection →

Natural Language Processing is the ability of a computer to interpret human language. So NLP it's not only about text processing but also about understanding sentences and documents. By giving computers the ability to interpret human language, we can build better human-machine interfaces, extract information from non-structured data, and increment automation. Like Apple's Siri and … Continue reading Machine Learning Applications: Natural Language Processing →

Computer Vision is a field of Artificial Intelligence that includes methods and techniques to enable computer programs to interpret and understand visual information, including images and videos. Typical Computer Vision tasks are image classification, object recognition, and Optical Character Recognition. The availability of large amounts of visual data, and computational power, combined with the advance … Continue reading Machine Learning Applications: Computer Vision →

We use Machine Learning for problems that traditional programming can't solve. In other words, we use Machine Learning for problems in which we can't infer a logical sequence of steps or rules to solve the problem. For example, let's say that we want to build a program to recognize the fruit contained in a picture. … Continue reading When should we use Machine Learning to solve a problem? →