What Is the Difference Between Machine Learning (ML) and Deep Learning (DL)?

Machine learning (ML) and deep learning (DL) represent a hierarchy of concepts and technologies in the field of artificial intelligence (AI). 

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Deep learning is part of machine learning, while machine learning itself is a component of the vast field of artificial intelligence.

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This AI hierarchy diagram can help you understand where ML and DL are positioned.

To understand how they are related, let's look at what each one does and how they work:

Machine Learning (ML)

• Broader Field: Machine learning is a wide area of study that includes many techniques and algorithms to help machines improve their performance using data. Examples of ML algorithms include linear regression, decision trees, support vector machines, clustering, and neural networks.
• Method Diversity: ML uses various algorithms, not just neural networks. These can be divided into three types. They are: supervised learning, unsupervised learning, and reinforcement learning. Each type has different applications and uses.
• Flexibility in Features and Scale: Many ML models need humans to select features, which are important parts of the data for the task at hand. These models are generally simpler than DL models. This makes them easier to understand but less powerful with very large or complex data.

Deep Learning (DL)

• Subset of ML: Deep learning is a subset of machine learning. It uses neural networks with many layers, called "deep" networks. These networks learn from large amounts of data in ways that are similar to how the human brain works.
• Data and Complexity Scale: DL handles large, complex data sets very well. It's especially good with unstructured data, like pictures, sounds, and text. DL models also find important features by themselves, which is useful when the data is too complex for humans to do it manually.
• Processing Power: DL usually needs more computer power than traditional ML. It often uses Graphics Processing Units (GPUs) or special hardware for training.

Practical Differences

• Application Areas: ML works well for simpler tasks with moderate-sized data sets. DL, on the other hand, shows its power in complex tasks like natural language processing (NLP) and object detection. Speech recognition, for which DL is also used, requires handling many layers of complexity, which is also true for translation between languages.
• Interpretability vs. Accuracy: ML models are usually easier to understand and it is easier to interpret their results. This is crucial in fields such as healthcare or finance. For example, a bank has to be able to explain to a client why it rejected their request for a loan. DL models often perform better in terms of accuracy and can handle raw data. However, they are harder to understand, and are commonly referred to as "black boxes."

Examples of Machine Learning (ML) Projects

Machine learning projects typically involve algorithms learning from data to make predictions or decisions.

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For instance, an online retailer like Amazon might use ML to suggest products to its customers. These suggestions are based on shoppers' buying and browsing history.

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Another common ML project is fraud detection in banking. Here, ML algorithms analyze transaction patterns to identify unusual activity that could indicate fraud. 

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ML is also seeing wide adoption in healthcare and medical research. 

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It can be used to predict patient diagnosis based on medical records and other health data. This helps doctors make more informed decisions.

Examples of Deep Learning (DL) Projects

Deep learning projects often deal with large and complex datasets.

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Such datasets often involve unstructured data such as images, audio, and text.

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For instance, a DL project might involve creating a convolutional neural network (CNN). The network will classify images into categories, and it does this by identifying objects in photos.

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Another example is a natural language processing (NLP) DL project. In this case a DL model is trained to comprehend and produce human language, which is useful for creating applications such as chatbots and translation tools.

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In autonomous driving, deep learning processes data from sensors to make immediate driving decisions, allowing vehicles to navigate safely in various traffic situations.

Conclusion

Knowing these differences helps you decide when to use traditional machine learning or deep learning.

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Your choice depends on the task's needs, the amount of data you have, the computing power available, and how important it is to understand the model's decisions.