Machine learning is the study of computer algorithms that improve automatically through experience or in short “making the computer learn patterns through repetition. There are several methods of machine learning outlined bellow:
Method #1:
Python’s Numpy Library The most beginner friendly way to start learning machine learning, with simple concepts and clean code.
Method #2:
Julia’s Flux Framework A more technical way of doing machine learning compared to Python Numpy, but it is fairly new and therefore has all the latest features you want.
Method #3:
C#’s ML.NET The “old-school” way of doing machine learning, but due to its age it means that everything is well documented and many bugs have been removed.
Method #4:
Python’s TensorFlow A more graphical approach to machine learning for Python compared to Numpy, and it also has a variety of Mathematical uses.
Method #5:
Java’s Deep Learning A method of machine learning focused for business and commercial use rather than scientific use using the Java programming language.
Method #6:
Javascript’s TensorFlow.js A lighter form of machine learning designed for use on webpages and other online services.
Method #7:
Python’s PyTorch This machine learning library made by Facebook is designed to train AI and help predict future changes in a system.
Method #8:
R Programming Language R is built-in with features that allow you to start machine learning without much extra fuss.
Method #9:
Apache Spark A language-universal method to do machine learning designed to deal with large amounts of data very very quickly.
Method #10:
Scala’s Breeze A less popular approach to machine learning that aims to mimic Java but fix the issues with machine learning in Java.
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