Machine Learning vs Deep Learning

Machine learning is a subset of artificial intelligence. It helps you use historical data to make better business decisions. Machine learning is also a process where machines take data, analyze it to generate predictions, and use those predictions to make decisions. Those predictions generate results, and those results are used to improve future predictions. Machine learning can make predictions from huge datasets. It can also optimize utility functions and extract hidden patterns and structures from those datasets by classifying data.

In contrast, deep learning is a subset of Machine learning. Deep-learning uses layers of non-linear Processing Units for features extraction and transformation. Each successive layer uses the output from the previous layer as an input. The algorithms may be supervised or unsupervised and applications include pattern analysis, which is unsupervised, and classification which could be supervised or unsupervised. These algorithms are also based on the unsupervised learning of multiple levels of features or representations of the data. Higher-level features are derived from low-level features to form a hierarchical representation. Deep learning algorithms are part of a broader Machine Learning field of learning representations of data, and they learn multiple levels of representations that correspond to different levels of abstraction. Where traditional Machine Learning focuses on feature engineering, Deep Learning focuses on end-to-end learning based on raw features.

Author - Mohit