In this Python Machine Learning tutorial for beginners we will look at:
1) How to hypertune machine learning model parameters
2) choose the best model for a given machine learning problem
We will start by comparing the traditional train_test_split approach with k-fold cross-validation. Next, we will see how GridSearchCV helps perform K Fold cross-validation with its handy API. GridSearchCV helps find the best parameters that deliver maximum performance. RandomizedSearchCV is another class in the sklearn library that does the same thing as GridSearchCV
but without performing an extensive search, this helps with computation time and resources. We will also see how to find the best model among all classification algorithms using GridSearchCV. In the end, we have an interesting exercise for you to solve.

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Exercise: https://github.com/codebasics/py/blob/master/ML/15_gridsearch/exercise.md
Code in this tutorial: https://github.com/codebasics/py/blob/master/ML/15_gridsearch/15_grid_search.ipynb

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Practice solution: https://github.com/codebasics/py/blob/master/ML/15_gridsearch/Exercise/15_grid_search_cv_exercise.ipynb

Topics covered in this video:
00:00 Introduction
00:45 train_test_split to find model performance
01:37 K-fold cross-validation
04:44 GridSearchCV for hyperparameter tuning
10:18 Randomized searchCV
12:35 Choosing the best model
15:25 Exercise

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Deep Learning Tutorial Python, Tensorflow and Keras: introduction and installation: https://www.youtube.com/watch?voPa20mUgJi8&listPLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index18

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