What Is Machine Learning?
Many machine-learning algorithms use structured data to train models.
Structured data is data organized in a specific format or structure such as spreadsheets and tables.
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Machine learning models are often used in various industries such as healthcare, e-commerce, finance, and manufacturing.
What Is Deep Learning?
Deep learning is a subfield of machine learning that focuses on training models by mimicking how humans learn.
Examples of unstructured data would be images, social media posts, videos, and audio recordings.
Although both methodologies have been used to train many useful models, they do have their differences.
On the other hand, machine learning will require significantly smaller amounts of data to make fairly accurate decisions.
Interpretability
Machine learning requires structured data as well as close developer intervention to make effective models.
This makes machine learning easier to interpret as developers are often part of the process when training the AI.
Deep learning uses artificial neural networks to learn from unstructured data such as images, videos, and sound.
This is why deep learning algorithms are often considered to be black box models.
There Are Other Machine Learning Subfields
You now understand the difference between machine learning and deep learning.
If so, then learning other machine learning subdomains should increase your efficiency to solve a problem.
