Deep Learning vs Machine Learning: Which One is the Best Fit for Your Use-Case?

Deep learning and machine learning are both subfields of artificial intelligence that are concerned with building systems that can learn from data and improve their performance over time. However, there are few key differences between these two:

Depth of learning: Deep learning is called “deep” because it typically involves the use of artificial neural networks which are composed of many layers, which enables them to learn complex patterns and relationships in the data. In contrast, machine learning algorithms may only have a few layers or may not use layers at all.

Types of learning: Deep learning algorithms are primarily used for supervised learning, where the system is trained on a labeled dataset and where it learns to map the input data to the correct output. Machine learning algorithms, on the other hand, can be used for both supervised as well as unsupervised learning, where the system must find patterns and relationships in the data by itself.

Applications: Deep learning is often used for tasks that include image and speech recognition, natural language processing, as well as predictive modeling, where it has achieved state-of-the-art results. Machine learning is more broadly applicable and is used for a wide range of tasks that include classification, clustering, and regression.

Overall, deep learning and machine learning are related but distinct fields, and the appropriate approach to use will depend on the specific task and the characteristics of the data. Both approaches have the potential to transform a wide range of industries and fields, and they are important tools for anyone working with data.

Algorithms for machine learning only utilise structured data. Humans must carry out the feature engineering stage if the data is unstructured. On the other hand, deep learning can also be used to process unstructured data.

Both machine learning and deep learning are essential in today’s environment. For small and medium-sized datasets, ML models work well. On the other hand, in order for deep learning models to produce reliable results, huge datasets are needed. In the end, it is entirely dependent upon your use case.