Machine Learning Applications: Medical Diagnosis

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

Classification Applications: Spam Detection

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

Machine Learning Applications: Natural Language Processing

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

Machine Learning Applications: Computer Vision

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

When should we use Machine Learning to solve a problem?

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?

The Best Algorithm to Start with Machine Learning

A good start point for Machine Learning is Linear Regression. By studying the Linear Regression algorithm, you will learn: Supervised LearningLoss FunctionsLoss Functions minimizationRegularization These concepts are not exclusive to Linear Regression. They apply to other learning algorithms as well. By creating and deploying Linear Regression models, you acquire engineering skills that are common to … Continue reading The Best Algorithm to Start with Machine Learning

What are Machine Learning Tasks? (part 2)

Machine Learning is a set of computational techniques that allows us to extract patterns from data. In other words, these algorithms can learn the information contained in datasets like tabular data, text, images, or even videos. However, there is no universal algorithm capable of learning every pattern from every kind of data. Each machine learning algorithm solves a specific type of learning problem. We call machine learning tasks the particular learning problems solved by machine learning algorithms.

What are Machine Learning Tasks? (part 1)

Machine Learning is a set of computational techniques that allows us to extract patterns from data. In other words, these algorithms can learn the information contained in datasets like tabular data, text, images, or even videos. However, there is no universal algorithm capable of learning every pattern from every kind of data. Each machine learning algorithm solves a specific type of learning problem. We call machine learning tasks the particular learning problems solved by machine learning algorithms.