Machine Learning vs Deep Learning: What's the Difference?

Machine learning and deep learning are related but distinct concepts, and understanding the difference is important for anyone pursuing AI training. Simply put, deep learning is a specialised subset of machine learning that uses neural networks with many layers.

Machine learning encompasses algorithms that learn patterns from data without being explicitly programmed. This includes decision trees, random forests, support vector machines, k-nearest neighbours, linear and logistic regression, and many others. These algorithms work well when you have structured data (like sensor readings in a spreadsheet) and a moderate amount of training data.

Deep learning uses artificial neural networks with multiple hidden layers to learn increasingly abstract representations of data. Deep learning excels when dealing with unstructured data (images, audio, text), when you have very large datasets, and when the patterns in the data are too complex for traditional algorithms to capture.

In industrial automation, both have their place. Classical machine learning is excellent for predictive maintenance on structured sensor data, process optimisation, and anomaly detection. Deep learning is superior for computer vision tasks like defect detection, for complex time-series forecasting, and for natural language processing applications. At EDWartens, our machine learning course in Bangalore covers both approaches with practical industrial examples.