Welcome to the fascinating world of Supervised Learning! 🌟 In this tutorial, we will explore the essential concepts and methodologies that form the core of supervised learning, a type of machine learning strategy where you train your models on known input-output pairs.
Key Concepts
- Labeled Data: Supervised learning requires a dataset with input/output pairs. The inputs are also called features, and a corresponding label is used for each input, which is also known as the target.
- Training and Testing: The dataset is typically divided into a training set and a testing set. The model is trained on the training set. Once the model achieves an acceptable level of accuracy, it is evaluated with the test set.
- Model: A function or a system that maps inputs to outputs. Common models include decision trees, regression models, and neural networks.
- Algorithms: Techniques like linear regression, support vector machines, and neural networks used to learn from data.
Applications
Some popular applications of supervised learning include:
- Image and Speech Recognition 🎤
- Medical Diagnosis 🏥
- Email Spam Detection 📧
- Market Prediction 📈
Froge is excited to learn with you! 🐸