All Machine Learning Beginner Mistakes explained in 17 Min

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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.

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:

================== 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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