Data is the main ingredient of Machine Learning models. An actual Machine Learning project starts with the team assessing if the necessary data is available. If not (which would be no surprise), the team needs to develop a strategy to collect and store the necessary data before modeling Machine Learning Algorithms. Common sources of data … Continue reading Building a Machine Learning Model: Collecting Data →

Deep Learning is a branch of Machine Learning specialized in Artificial Neural Networks with multiple intermediary layers.  Neural Networks with multiple layers can approximate very complex non-linear functions. That's why Deep Learning has been very successful in CV (Computer Visions), NLP (Natural Language Processing), and Reinforcement Learning.  Deep Learning architectures include: Convolutional Neural NetworksRecurrent Neural … Continue reading Types of Machine Learning: Deep Learning →

The typical regression task predicts the value of a target variable based on the values of one or more feature variables. For example, to predict the price of a house based on its characteristics like size, number of rooms, etc. But in some cases, we want to predict the value of a variable based on … Continue reading Types of Machine Learning: Time Series Analysis →

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 →