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Source channel @githubtrending · Post #14869 · Jun 26

#html#data_science#education#machine_learning#machine_learning_algorithms#machinelearning#machinelearning_python#microsoft_for_beginners#ml#python#r#scikit_learn#scikit_learn_python Microsoft’s "Machine Learning for Beginners" is a free, 12-week course with 26 lessons designed to teach classic machine learning using Python and Scikit-learn. It includes quizzes, projects, and assignments to help you learn by doing, with lessons themed around global cultures to keep it engaging. You can access solutions, videos, and even R language versions. The course is beginner-friendly, flexible, and helps build practical skills step-by-step, making it easier to understand and apply machine learning concepts in real-world scenarios. This structured approach boosts your learning retention and prepares you for further study or career growth in ML[1][5]. https://github.com/microsoft/ML-For-Beginners

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Interesting Planet 🌍

@interesting_planet_facts · Post #1053 · 11/19/2025, 06:11 PM

🌎 In 1977, the Soviet Venera 14 probe recorded mysterious low-frequency “thunder”-like sounds on Venus. Scientists now attribute these to seismic activity or wind interacting with the planet’s dense atmosphere. Venus’s surface winds move slowly, but thick air carries sound much farther than on Earth. ✨ #Venus⚡#sounds⚡#space 👉subscribe Interesting Planet 👉more Channels ​

djangoproject

@djangoproject · Post #255 · 02/02/2017, 06:57 PM

https://github.com/tyiannak/pyAudioAnalysis #pyAudioAnalysis is a Python library covering a wide range of audio analysis tasks. Through pyAudioAnalysis you can: Extract #audio features and representations (e.g. mfccs, spectrogram, chromagram) Classify unknown #sounds Train, parameter tune and evaluate classifiers of audio segments Detect audio events and exclude silence periods from long recordings Perform supervised segmentation (joint segmentation - classification) Perform unsupervised segmentation (e.g. speaker diarization) Extract audio thumbnails Train and use audio regression models (example application: emotion recognition) Apply dimensionality reduction to visualize audio data and content similarities