All Machine Learning Beginner Mistakes explained in 17 Min

All Machine Learning Beginner Mistakes explained in 17 Min ######################################### I just started my own Patreon, in case you want to supp...

Infinite Codes86.6K views18:02

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All Machine Learning Beginner Mistakes explained in 17 Min ######################################### I just started my own Patreon, in case you want to support! Patreon Link: https://www.patreon.com/c/InfiniteCodes ######################################### Don’t make the same mistakes I made! Here is a list of things to avoid when starting Machine Learning and Data Science. Also Watch: Learn Machine Learning Like a GENIUS and Not Waste Time https://youtu.be/qNxrPri1V0I All Machine Learning Concepts Explained in 22 Minutes https://youtu.be/Fa_V9fP2tpU All Machine Learning algorithms explained in 17 min https://youtu.be/E0Hmnixke2g The Math that make Machine Learning easy (and how you can learn it) https://youtu.be/wOTFGRSUQ6Q 15 Machine Learning Lessons I Wish I Knew Earlier https://youtu.be/espQDESe07w Machine Learning Playlist: https://www.youtube.com/watch?v=wOTFGRSUQ6Q&list=PLbdTl8vSSyUDAvDPc1r3j9itciu_kb5vG&ab_channel=InfiniteCodes Git/Github Playlist: https://www.youtube.com/watch?v=ZFFtMyOFPe8&list=PLbdTl8vSSyUBJg6PI9AqfJBw8U0y9J3kY&ab_channel=InfiniteCodes ================== Timestamps ================ 00:00 - Intro Data-Related Issues 00:36 - Not cleaning your data properly 01:20 - Forgetting to normalize/standardize 01:59 - Data leakage 02:38 - Class imbalance issues 03:17 - Not handling missing values correctly Model Training 04:03 - Using wrong metrics 04:55 - Overfitting/underfitting 05:38 - Wrong learning rate 06:08 - Poor hyperparameter choices 06:58 - Not using cross-validation Implementation 07:29 - Train/test set contamination 08:25 - Wrong loss function 08:58 - Incorrect feature encoding 09:54 - Not shuffling data 10:19 - Memory management issues Evaluation 10:40 - Not checking for bias 11:12 - Ignoring model assumptions 12:05 - Poor validation strategy 12:31 - Misinterpreting results Common Pitfalls 13:43 - Using complex models too early 14:52 - Not understanding the baseline 15:47 - Ignoring domain knowledge 16:46 - Poor documentation 17:15 - Not version controlling

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Dec 20, 2024

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