Supervised Learning Techniques: Background Supervised learning is a machine learning technique where a model learns from labeled data. Since every data input is paired with a corresponding output label, the model can iteratively test various weights and optimize those weights using the known ‘correct’ prediction. This allows the model to pull out complicated patterns from historical data with the goal of predicting the output for new, unseen data. Contrast this with unsupervised learning where the model uses unlabeled data to find patterns without explicit guidance.

Supervised Learning Techniques: Background Supervised learning is a machine learning technique where a model learns from labeled data. Since every data input is paired with a corresponding output label, the model can iteratively test various weights and optimize those weights using the known ‘correct’ prediction. This allows the model to pull out complicated patterns from historical data with the goal of predicting the output for new, unseen data. Contrast this with unsupervised learning where the model uses unlabeled data to find patterns without explicit guidance.

Image Classification: Background Image classification is a widely used type of machine learning with numerous practical applications ranging from face recognition (security) to medical image analysis (diagnostic) to wildlife monitoring (conservation). Convolutional neural networks (CNNs) are frequently used for image classification, in part because of the easy-to-use and well-supported TensorFlow and PyTorch python packages. Additionally CNNs require very little data pre-processing (feature extraction is automated), making them an ideal choice for image classification problems.