Machine Learning

Demystifying the Confusion Matrix: A Deep Dive into Evaluating Model Performance In the world of Machine Learning, understanding model performance is essential. One powerful tool for this purpose is the Confusion Matrix, a simple yet highly effective table layout for visualization and comprehension of your classifier’s performance. The confusion matrix places the model’s predictions against the ground-truth data labels, creating an intuitive comparison grid. Each cell in this matrix represents a different aspect of the model’s performance, namely True Negatives (TN), False Negatives (FN), False Positives (FP) and True Positives (TP)....

Overview of Collaborative and Content-based Filtering in Recommender Systems Recommender systems have become integral to modern technology applications, spanning diverse industries from e-commerce to media streaming services. At the heart of these systems are filtering algorithms that power the delivery of personalized suggestions to each user. Two prominent types of filtering techniques are Collaborative Filtering and Content-based Filtering. Key Terms: Collaborative Filtering: This approach models recommendations based on a user’s past behavior, such as items previously purchased or selected, as well as numerical ratings given to those items....

Understanding Different Classification Loss Function Types in Machine Learning In machine learning, classification problems are quite common and central to many applications. The purpose of a classification problem is to predict discrete class labels, such as detecting if an email is spam or not, identifying the species of a flower based on measurements, or recognizing handwritten digits. While designing a classification model, a key part to consider is the choice of the loss function, which measures how well the model’s predictions align with the true values....

Azure Applied AI Services Azure Metrics Advisor Analyze your business performance data and detect anomalies. Azure Cognitive Search Enrich data in your search indexes by using AI to analyze vision, language, and speech in content. Azure Immersive Reader Improve access to your web applications for new readers, language learners, and people with learning differences, such as dyslexia. Azure Bot Service Create bots that can converse with your customers and partners and respond to their queries....

Azure - AutoML Process Process Prepare data Train model Evaluate performance Deploy a predictive service Prepare Data Import data from Azure storage Local files SQL databases Web files Azure Open Datasets Train model Classification (predicting categories or classes) Regression (predicting numeric values ) Time series forecasting (predicting numeric values at a future point in time) Natural language processing Computer vision Evaluate performance Cross validation RMSE (Root Mean Squared Error) NRMSE (Normalized Root Mean Squared Error) Residual History Predicted vs True chart Deploy Azure Container Instances (ACI) Azure Kubernetes Service (AKS)