Avoid These 17 Common Machine Learning Beginner Mistakes in Just 17 Minutes 🚀

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:
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