All Machine Learning Beginner Mistakes explained in 17 Min скачать в хорошем качестве

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 support! Patreon Link:   / 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    • Learn Machine Learning Like a GENIUS and N...   All Machine Learning Concepts Explained in 22 Minutes    • All Machine Learning Concepts Explained in...   All Machine Learning algorithms explained in 17 min    • All Machine Learning algorithms explained ...   The Math that make Machine Learning easy (and how you can learn it)    • How Math makes Machine Learning easy (and ...   15 Machine Learning Lessons I Wish I Knew Earlier    • 15 Machine Learning Lessons I Wish I Knew ...   Machine Learning Playlist:    • How Math makes Machine Learning easy (and ...   Git/Github Playlist:    • How to clone GitHub Repository (2024 updated)   ================== 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