Machine Learning vs Deep Learning

# Machine Learning vs Deep Learning Deep learning is a subset of machine learning, not a separate field — but the practical differences in how each works matter when deciding what approach fits a given problem. ## Traditional Machine Learning Classic ML algorithms — decision trees, linear regression, support vector machines — often require manually engineered features. For a house price prediction model, someone decides which inputs matter: square footage, location, number of rooms. ## Deep Learning Deep learning uses neural networks with multiple layers that learn relevant features directly from raw data, without a human manually deciding what matters. This is why deep learning excels at images and text, where manually defining useful features is extremely difficult. ## Data Requirements Traditional ML can work well with relatively small, structured datasets. Deep learning typically needs much larger datasets to perform well — a neural network trained on a few hundred examples usually underperforms a well-tuned traditional model on the same data. ## Compute Requirements Deep learning models, especially larger ones, require significantly more computational power (often GPUs) for training compared to traditional ML algorithms that can run efficiently on standard CPUs. ## Interpretability A decision tree's logic can be inspected and explained directly. Deep neural networks are largely "black boxes" — this matters in regulated industries like finance or healthcare where decisions need to be explainable. ## Choosing Between Them For structured, tabular data with a limited dataset, traditional ML is often the more practical and interpretable choice. For unstructured data at scale — images, audio, natural language — deep learning tends to significantly outperform traditional approaches. ## Conclusion Neither approach is universally "better" — the right choice depends on data type, dataset size, and how much model interpretability actually matters for the problem.