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Quick Guide to Understanding Machine Learning: Key Terms for Beginners
- AI is about teaching machines to think or act smart like humans, while ML involves machines learning patterns from data.
- Data is the raw input used to train, validate, and test models, while an algorithm is a set of rules followed by machines to find patterns.
- A model is the machine learning algorithm's output, mapping inputs to outputs, and Training Data is used for training models.
- Features are measurable properties or inputs used to predict the target variable, with Feature Engineering involving improving model performance.
- Bias refers to error due to an overly simplistic model, while variance is error due to model sensitivity.
- The Bias-Variance Tradeoff aims to find a balance between complexity and simplicity to minimize total error.
- Overfitting is when a model performs well on training but poorly on testing, while underfitting means the model didn't learn enough.
- Batch, Epoch, and Iteration refer to different stages in the training process, and Parameters are the model's learned aspects.
- Gradient Descent is an optimization method to minimize cost by adjusting model parameters, and Evaluation Metrics measure model performance.
- Precision, Recall, and F1 Score are metrics for assessing model performance, with a Confusion Matrix showing classification results.
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