All Machine Learning Concepts Explained in 22 Minutes

All Basic Machine Learning Terms Explained in 22 Minutes ######################################### I just started my own Patreon, in case you want to support! Patreon Link: https://www.patreon.com/c/InfiniteCodes ######################################### To get you out of your confusion with all the Machine Learning Vocabulary, here an overview of all basic terms you will encounter as you start your Journey in Machine Learning and Data Science. Also Watch: All Machine Learning algorithms explained in 17 min https://youtu.be/E0Hmnixke2g Learn Machine Learning Like a GENIUS and Not Waste Time https://youtu.be/qNxrPri1V0I 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 ================== Timestamps ================ 00:04 - Artificial Intelligence (AI) 00:37 - Machine Learning 01:30 - Algorithm 02:06 - Data 02:48 - Model 03:30 - Model fitting 03:44 - Training Data 04:17 - Test Data 04:54 - Supervised Learning 05:24 - Unsupervised Learning 06:01 - Reinforcement Learning 07:05 - Feature (Input, Independent Variable, Predictor) 07:45 - Feature engineering 08:15 - Feature Scaling (Normalization, Standardization) 08:48 - Dimensionality 09:34 - Target (Output, Label, Dependent Variable) 09:59 - Instance (Example, Observation, Sample) 10:32 - Label (class, target value) 11:16 - Model complexity 12:15 - Bias & Variance 13:23 - Bias Variance Tradeoff 14:11 - Noise 14:30 - Overfitting & Underfitting 15:20 - Validation & Cross Validation 16:20 - Regularization 16:40 - Batch, Epoch, Iteration 17:40 - Parameter 18:22 - Hyperparameter 18:50 - Cost Function (Loss Function, Objective Function) 19:39 - Gradient Descent 20:49 - Learning Rate 21:28 - Evaluation