5 Key Differences
Hardware
Deep Learning(DL) systems require much more powerful hardware when compared to Machine Learning(ML) systems. This is because DL systems require to process large amounts of data and considers the complexity of the mathematical calculations involved in the algorithms.
Time Consumption
Deep learning systems can take a lot more time to train than Machine learning systems due to the huge data sets a deep learning system requires, and because of the involvement of many parameters of complicated mathematical formulas.
Performance
ML systems show good performance on structured & small-scale data as the algorithms tend to parse data in parts & combine those parts for solutions. And, DL systems perform exceptionally well on large-scale & unstructured data as they look at an entire problem/scenario in one go.
Applications
Usually, ML applications include predictive programs such as Weather or Stock Market forecasting. Meanwhile, DL applications include complex tasks such as Image or Voice recognition as DL systems use many layers of neural networks
Human Intervention
ML systems require domain knowledge and feature engineering skills, indicating dependency & human intervention while a Deep learning system tries to learn those features without additional human intervention.