Machine learning, on the other hand, relies on algorithms based in mathematics and statistics-not neural networks-to find patterns.
Offer recommendations, given you like one restaurant/movie/product, here’s another you will likely enjoy.
Predict routine behavioral actions, like when a person might cancel their gym membership.
Generate full-length, almost coherent articles on a given subject.
Manipulate images, like with deep fakes.
Large amounts of numbered and categorical data.
In picking a tool, you need to ask what is your goal: machine learning or deep learning? Deep learning has come to mean using neural networks to perform many tasks to analyze data: Python is the predominant machine learning programming language. Nearly all ML the frameworks-those we discuss here and those we don’t-are written in Python. There are a variety of machine learning frameworks, geared at different purposes. An ML framework is any tool, interface, or library that lets you develop ML models easily, without understanding the underlying algorithms. Unless you’re a data scientist or ML expert, these algorithms are very complicated to understand and work with.Ī machine learning framework, then, simplifies machine learning algorithms. In this article, we take a high-level look at the major ML frameworks ones-and some newer ones to the scene: Given that each takes time to learn, and given that some have a wider user base than others, which one should you use? There are many machine learning frameworks.