All Machine Learning Beginner Mistakes explained in 17 Min
All Machine Learning Beginner Mistakes explained in 17 Min
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Don’t make the same mistakes I made! Here is a list of things to avoid when starting Machine Learning and Data Science.
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================== 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