Before we go into the finding out in detail the basic difference between deep learning and classical machine learning, we must know what these two terms mean. The terms Deep Learning and Classical Machine Learning may seem to fall under the same broad heading, and that may be one of the reasons why they are often confused with each other. That is why defining them is necessary for its proper usage.

Classical Machine Learning

Classical Machine Learning involves the use of various algorithms to obtain data, to learn from that data and to make decisions based on the data and information obtained and learned.

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Deep Learning

Deep Learning, also known as the Deep Structured Learning or Hierarchical Learning is a family of methods of Machine learning compiled together, based on various learning data representations. In Deep Learning, the algorithms are structured in layers in creating an artificial neural network, similar to the neural network in the human brain.

Comparison of Deep Learning and Classical Machine Learning

Deep Learning is the first technique that comes to one’s mind when it comes to the topic of Artificial Intelligence because of the superior performance over the classical methods in a variety of tasks of the machine, such as natural language, playing games, speech, vision, etc. Although Deep Learning has some advantages over Machine Learning, the latter has also proven to be better than the former on different scales.

• Performance- The classical machine learning methods have been left far behind when it comes to tasks such as that of speech, playing games, vision, language and so on when compared to Deep Learning. The neural networks used by deep learning have made it impossible for the classical methods to even compete with it.
• Dealing With Data- The networks used by the deep learning methods are way more useful than the algorithms used by Classical methods. The accuracy of the data in deep networks can be easily improved by just adding more data whereas this cannot be done in the case of Classical methods. 
• No More Feature Engineering- Where classical machine learning methods require a complex feature engineering of the algorithms; the deep learning methods do not need any such complex techniques. One can directly pass on the data to the network and easily achieve better performance without much hard work. This is not as easy in case of Classical Machine learning. 
• Use On Small Data- When it comes to handling and dealing with small data, classical machine learning is more effective than the deep learning methods. Deep Learning requires a specifically large set of data to achieve a high performance, which is not a compulsion in case of the classical machine learning technique.
• Effective Costing- The classical machine learning is computationally and financially cheaper than the deep learning technique. Deep learning requires a high-end GPU to handle the relatively large set of data into the system. The GPUs are generally quite expensive, and without them, effective deep learning is not possible. This problem does not arise in the case of Classical Machine Learning.  

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On reading the comparisons between Classical Machine Learning and Deep learning methods, it becomes easier for one to choose the best option for achieving better performance!