How do machine learning, deep learning, and NLP differ in practice?

Machine learning (ML) is the broad discipline of building systems that improve through experience. It includes supervised learning (predicting outcomes from labeled data), unsupervised learning (finding structure in unlabeled data), and reinforcement learning (learning through trial and error).

Deep learning (DL) is a subset of ML that uses neural networks with multiple layers to automatically learn hierarchical representations. Deep learning excels at tasks involving images, audio, and sequences where hand-crafted features are impractical. It requires more data and compute but can capture complex, non-linear patterns that traditional ML struggles with.

Natural language processing (NLP) applies ML and DL techniques specifically to human language. Classical NLP used rule-based systems and statistical models. Modern NLP leverages transformers and large language models (LLMs) that learn contextual representations of words and sentences, enabling tasks like translation, summarization, and question answering.

In practice, the choice depends on your problem: tabular business data often works well with traditional ML (e.g., gradient boosting), vision tasks favor convolutional neural networks, and language tasks increasingly use transformer architectures. Understanding these distinctions helps teams select the right tool and avoid over-engineering.