Supervised learning is a type of machine learning in which a model is trained on labeled data. This means that the training data includes both the input data and the corresponding correct output, and the goal of the model is to make predictions on new data that it has not seen before. In supervised learning, the model is able to learn from the training data by finding patterns and relationships between the input and the output, and then using this knowledge to make predictions on unseen data. Some common examples of supervised learning tasks include classification, regression, and sequence labeling. Supervised learning is a powerful and widely-used technique in the field of machine learning, and it has many applications in areas such as natural language processing, computer vision, and speech recognition. Examples of supervised learning algorithms include regression, naive Bayes, decision trees, decision forests, and support vector machines.
Have you ever wondered how Facebook predicts the future? Meet Facebook Prophet, the open-source time series forecasting tool developed by Facebook’s Core Data Science team. Built on top of the PyStan library, Facebook Prophet offers a simple and intuitive interface for creating forecasts using historical data. What sets Facebook Prophet apart is its highly modular … Read more
Content-based recommender systems are a popular type of machine learning algorithm that recommends relevant articles based on what a user has previously consumed or liked. This approach aims to identify items with certain keywords, understand what the customer likes, and then identify other items that are similar to items the user has previously consumed or … Read more
Perfecting your machine learning model’s hyperparameters can often feel like hunting for a proverbial needle in a haystack. But with the Random Search algorithm, this intricate process of hyperparameter tuning can be efficiently automated, saving you valuable time and effort. Hyperparameters are properties intrinsic to your model, like the number of estimators in an ensemble … Read more
Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and thus deliver a more detailed … Read more
In this tutorial, we’ll be using machine learning to predict and map out crime in San Francisco. We’ll be working with a dataset from Kaggle that contains information on 39 different types of crimes, including everything from vehicle theft to drug offenses. Using Python and the powerful Scikit-Learn library, we’ll train a classification model using … Read more
Time series analysis and forecasting is a tough nut to crack, but the ARIMA model has been cracking it for decades. ARIMA, short for “Auto-Regressive Integrated Moving Average,” is a powerful statistical modeling technique for time series analysis. It’s particularly effective when the time series you’re analyzing follows a clear pattern, like seasonal changes in … Read more
This tutorial shows how to use Convolutional Neural Networks (CNNs) with Python for image classification. CNNs belong to the field of deep learning, a subarea of machine learning, and have become a cornerstone to many exciting innovations. There are endless applications, from self-driving cars over biometric security to automated tagging in social media. And the … Read more
Customer retention is a prime objective for service companies, and understanding the patterns that lead to customer churn can be the key to maintaining long-lasting client relationships. Businesses incur significant costs when customers discontinue their services, hence it’s vital to identify potential churn risks and take preemptive actions to retain these customers. Machine Learning models … Read more
Are you struggling to find the best hyperparameters for your machine learning model? With Python’s Scikit-learn library, you can use grid search to fine-tune your model and improve its performance. In this article, we’ll guide you through the process of hyperparameter tuning for a classification model, using a random decision forest that predicts the survival … Read more
Are you interested in learning how multivariate forecasting models can enhance the accuracy of stock market predictions? Look no further! While traditional time series data provides valuable insights into historical trends, multivariate forecasting models utilize additional features to identify patterns and predict future price movements. This process, known as “feature engineering,” is a crucial step … Read more
Are you ready to learn about the exciting world of social media sentiment analysis using Python? In this article, we’ll dive into how companies are leveraging machine learning to extract insights from Twitter comments, and how you can do the same. By comparing two popular classification models – Naive Bayes and Logistic Regression – we’ll … Read more
