Part 5 – Supervised Machine Learning 🧠🤖📊 #AI #supervisedlearning #youtubevideo
Supervised machine learning is a type of machine learning where the algorithm is trained on a labeled dataset, which means that each input data point is associated with a corresponding output label. The goal of supervised learning is to learn a mapping from inputs to outputs so that the algorithm can make accurate predictions or classifications on new, unseen data.
Here’s a basic overview of the key components of supervised machine learning:
1) Dataset –
Training Data: This is the labeled dataset used to train the machine learning model. It consists of input-output pairs, where the input is the feature or set of features, and the output is the corresponding label or target.
Testing Data: This is a separate set of data that the model has not seen during training. It is used to evaluate the model’s performance on new, unseen examples.
2) Model:
The model is the algorithm or mathematical function that learns the mapping between inputs and outputs based on the training data. The model can be of various types, such as linear regression, decision trees, support vector machines, neural networks, etc.
3) Training:
During the training phase, the model is presented with the labeled training data, and it adjusts its internal parameters to minimize the difference between its predictions and the actual labels. This process involves an optimization algorithm that fine-tunes the model.
4) Prediction:
After training, the model can make predictions or classifications on new, unseen data. Given a set of features as input, the model produces an output or a predicted label.
Supervised learning can be categorized into two main types based on the nature of the output variable:
Regression: When the output variable is a continuous value. For example, predicting house prices, temperature, or sales.
Classification: When the output variable is a category or label. For example, spam detection, image recognition, or sentiment analysis.
Supervised learning is widely used in various applications across different industries due to its ability to make predictions based on historical data.
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