Machine Learning Vs. Deep Learning: What Is The Difference?


Evelyn Miller

Evelyn Miller is an experienced writer and Data Science Lead - Business Partner at Magnimind Academy. Find Evelyn on LinkedIn and Twitter.

Two of the most talked-about subfields of artificial intelligence (AI) are machine learning and deep learning. They are not the same thing, even though they are frequently used interchangeably. Businesses and organizations looking to implement AI-based solutions need to know the difference between the two.

A subfield of artificial intelligence (AI) that focuses on the creation of algorithms and statistical models that enable computers to carry out activities that typically call for human intelligence is known as machine learning. Prediction, pattern recognition, and decision-making are some of these tasks. Algorithms for machine learning make predictions based on historical data and identify patterns in data using mathematical and statistical models.

In contrast, deep learning is a subfield of machine learning that draws inspiration from the human brain’s structure and operation. Using artificial neural networks to process and analyze large amounts of data, deep learning algorithms attempt to imitate the human brain’s functions. These networks are made up of multiple layers of nodes that are connected to one another. Each layer takes information and sends it to the next layer.

The way they solve problems is one of the main differences between machine learning and deep learning.

Deep learning algorithms are designed to analyze and learn from data in a manner that mimics the way the human brain processes information, whereas machine learning algorithms are designed to analyze data and make predictions based on statistical models.

Deep learning may extract its own features from the data whereas machine learning requires features to be given in terms of data.

The kind of data they are best suited to process is another important difference between the two.

Deep learning algorithms are better suited for unstructured data like images, videos, and audio, whereas machine learning algorithms are typically used for structured data like numerical or categorical data.

This is due to the fact that deep learning algorithms are able to identify patterns in intricate data that traditional machine learning algorithms have trouble capturing.

The model’s utilized level of complexity is another significant distinction. Deep learning algorithms employ much more complex models, such as artificial neural networks, whereas machine learning algorithms typically employ relatively straightforward models, such as decision trees or linear regression. Deep learning algorithms can now handle a lot of data and make better predictions thanks to this.

In conclusion, although machine learning and deep learning are both potent subfields of artificial intelligence, their methods, data types, and model complexity all differ. For businesses and organizations to select the AI-based solution that is most suitable for their particular requirements, it is essential to comprehend these distinctions. Deep learning and machine learning both have the potential to significantly alter our lives and revolutionize a variety of industries.

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To learn more about variance and bias, click here and read our another article.

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