Python is one of the most common used languages for DSP, ML, and data science

The ease of use and large library ecosystem makes Python a good choice for developers who are implementing their own DSP algorithms, or developing and training their own ML neural networks. Here are some Python tutorials to help you get started.

Short development time and strong library support

Learn about the most common Python libraries for tasks ranging from cryptography to DSP.

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DSP API for microcontrollers

To allow an algorithm to run faster on your embedded target, learn how to code a control system in Python using Numpy and SciPy and replace your functions with the CMSIS-DSP library.

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FIR filter

Design a linear phase DSP application with Python, for a system that does not require a feedback component, using the Mbed DSP API.

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Machine learning

Image classification

Learn how to classify images of clothing using Python, the TensorFlow library, Keras, and Google Colab.

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Classical ML

Use the scikit-learn library to perform hand-written digit classification with classical ML techniques.

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MicroPython

First steps

Start learning about MicroPython with the BBC micro:bit.

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Getting started with the pyBoard

Program servo motors, LEDs, touch sensors, and more with a PyBoard based on an Arm Cortex-M4 microcontroller.

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Python resources