Python and Python Libraries

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What is Python?

Python is one of the most, if not the most used programming language. It's a high-level, relatively easily readable general-purpose language. It works on multiple platforms such as Windows, Mac, Linux, and Raspberry Pi. It's an interpreted language, which means that the code can be executed as soon as it is written. This allows for fast prototyping.

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Libraries are reusable collections of files, which contain functions to be used in other programs. Using pre-existing libraries means that you won't need to start from scratch every time.

Useful Python Libraries for ML

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NumPy

NumPy

NumPy offers multi-dimensional array and matrix processing capabilities and high-level mathematical functions. It is used by many other extensions.

SciPy library and SciPy stack

SciPy library and SciPy stack

Interpolation, optimization, statistics, and linear algebra are some of the numerical routines that the SciPy library offers. It is a part of a larger SciPy stack, which contains NumPy, SciPy library, pandas, SumPy, IPython, Matplotlib, and others.

scikit-learn

scikit-learn

Scikit-learn is an open-source python library that provides functionality for supervised- and unsupervised learning, data analysis, and dataset transformations. It's built on top of NumPy, SciPy, and matplotlib.

TensorFlow

TensorFlow

An open-source platform for machine learning, developed by the Google Brain team. TensorFlow offers tools, libraries, and community resources for creating ML solutions. The TensorFlow site also offers guides from beginner level to advanced, on how to start your first neural network projects and more.

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Watch the video above and find out what tools and libraries were used and for what purposes. Can you find alternatives on the internet? How do these tools compare to some other popular tools?

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