All Machine Learning Beginner Mistakes explained in 17 Min ######################################### I just started my own Patreon, in case you want to support! Patreon Link: ######################################### 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 All Machine Learning Concepts Explained in 22 Minutes All Machine Learning algorithms explained in 17 min The Math that make Machine Learning easy (and how you can learn it) 15 Machine Learning Lessons I Wish I Knew Earlier Machine Learning Playlist: Git/Github Playlist: ================== 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