All About AI: Hash, GeoHash, Binary & More: 5 Categorical Encoding Tricks for Your ML Model Discover the power of encoding techniques in machine learning with this detailed guide! In this video, we explore five essential encoding methods—Hash Encoding, Geo Hash Encoding, Gray Encoding, Base N Encoding, and Binary Encoding—to convert categorical data (like temperature, color, and city names) into numerical values that machine learning models can understand. Learn how each technique works, its benefits, drawbacks, and real-world use cases, including text data processing, location-based apps, hardware applications, and tree-based models like LightGBM and XGBoost. We dive into practical examples using a simple table with categories like temperature and color, showing how to transform them into numbers with Python code. Follow along as we implement these encodings step-by-step using libraries like pandas, category_encoders, and geohash, with a focus on high-cardinality data, dimensionality reduction, and model performance optimization. Whether you're dealing with big data, spatial proximity, or compact representations, this video has you covered! Perfect for beginners and advanced learners, this tutorial includes a visual demonstration using Manim to animate the encoding process for temperature values. Boost your ML skills by choosing the right encoding technique for your project—hash for scalability, geo hash for location clustering, gray for hardware stability, base n for memory constraints, or binary for tree-based models. Don’t miss the Python code walkthrough to see these techniques in action! Subscribe for more machine learning tutorials and hit the like button if you found this helpful! Let us know in the comments which encoding you’ll use next! Timestamps: • 0:00 - Introduction to Encoding Techniques • 0:40 - Hash Encoding Explained • 1:48 - Geo Hash Encoding for Locations • 2:50 - Gray Encoding for Hardware • 4:20 - Base N Encoding Overview • 5:47 - Binary Encoding for ML Models • 7:31 – Comparison of 5 methods • 8:24 – Choosing the Right Encoding from these 5 methods • 9:23 - Python Implementation with Code • 16:00 - Choosing the Right Encoding • 14:30 – Manim visualization and Conclusion For more videos on categorical variable encoding, you can bookmark this playlist: https://youtube.com/playlist?list=PLSOxlehGDVekco2u5EFQaLtsQ5ltByoQi&si=23mxubE9wRkORhmR For Video on Advanced level AI (AI Practitioner) you can watch video playlist: https://youtube.com/playlist?list=PLSOxlehGDVelNTUs8IEtkpgE0ySwAysXe&si=sj-nYswCRhH39HtY For Video on All about AI basic level tutorial (AI Enthusiast) you can follow below playlist: https://youtube.com/playlist?list=PLSOxlehGDVekaOfY5TMmI5MS1zbNJLUFL&si=9i3d03xIxO4ghYMK If you are interested in Generative AI , please follow this playlist: https://youtube.com/playlist?list=PLSOxlehGDVen1UFY0daIegmmZHm3jjQ_Y&si=3TCTX-87q58KUXju If you are looking for videos on book summary, about life, psychology and philosophy, you can follow this playlist: https://www.youtube.com/playlist?list=PLSOxlehGDVelY34fDoL3qtppXuzmvo5Of For videos on AI , Machine Learning and Data Science, follow this playlist: https://www.youtube.com/playlist?list=PLSOxlehGDVenLrEROl1JxkNuCIBVeddHu https://youtu.be/8KwmesGwhXo #MachineLearning #EncodingTechniques #HashEncoding #GeoHashEncoding #GrayEncoding #BaseNEncoding #BinaryEncoding #CategoricalData #DimensionalityReduction #PythonML #HighCardinality #MLPreprocessing #TreeBasedModels #LightGBM #XGBoost #LocationEncoding #HardwareEncoding #DataTransformation #ManimAnimation #mltutorial machine learning encoding, hash encoding, geo hash encoding, gray encoding, base n encoding, binary encoding, categorical data, dimensionality reduction, Python machine learning, high cardinality data, ML preprocessing, tree-based models, LightGBM, XGBoost, location-based encoding, hardware encoding, data transformation, Manim animation, ML tutorial, encoding techniques explained via YouTube https://www.youtube.com/watch?v=8KwmesGwhXo
