Duration: 1:42:56

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An introduction to the scikit library. In this video we will look at the iris data set. We will convert the Bunch into a DataFrame and Saving to an Excel File
and then Import the Excel File as a DataFrame. We will look at Data Visualization, the Label Encoder, the Standard Scalar Transformer Class, the Principle Component Analysis Transformer Class and the Isomap Transformer Class.

We will use the K Nearest Neighbors Classifier Estimator Class and then look at using the Train-Test Split Function and Evaluation Metrics such as the Accuracy Score and Confusion Matrix Function. We will then look at the Variance of Accuracy Score and then the K-Fold Cross Validation function. We will then use the Grid Search Cross Validation Model Class to look for the best parameters for the estimator and the Randomized Search Cross Validation Model Class in order to save time.

We will then discuss the Curse of Dimensionality and look at other estimators such as the Naive Bayes Classifier, the Decision Tree Classifier, the Random Forest Classifier Model, the Support Vector Classifier and the Logistic Regression.

Written Instructions:
https://dellwindowsreinstallationguide.com/scikit-learn/

Note you should make sure you are fluent with the Core Python library, the Numeric Python Library (numpy), the matplotlib plotting library and the Python and Data Analysis Library (pandas) before beginning with the SciKit Learn (sklearn) library. In this video I will use the Spyder 4 IDE. You should already have it installed with Anaconda which has the necessary DataScience Libraries. More details can be found at:
https://dellwindowsreinstallationguide.com/python/
https://www.youtube.com/watch?v=ME5JTu9wrJA
https://www.youtube.com/watch?v=6V5pqwmUMMc
https://www.youtube.com/watch?v=NfoDqTixFAs
https://www.youtube.com/watch?v=MohDi9XMOm0
https://www.youtube.com/watch?v=Wyq-z4pwgjA
#sklearn #scikit #python