Machine Learning with Scikit-Learn - Part 40 - One Hot Encoding 1

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In this machine learning tutorial we learn about One-Hot-Encoding, which is one of the ways to represent categorical variables.

A One-Hot Encoder replaces categorical variables/features with one or more features that can only take numerical values. We are going to use a One-Hot Encoder on the 'adult income' dataset.

The code: https://github.com/CristiVlad25/ml-sklearn/blob/master/Machine%20Learning%20with%20Scikit-Learn%20-%2040%20-%20One%20Hot%20Encoding%20-%201.ipynb

The Dataset:

https://archive.ics.uci.edu/ml/machine-learning-databases/adult/

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References:

1. Prateek Joshi: Artificial Intelligence with Python - https://amzn.to/2X1Mt0q

Books I recommend to learn Machine Learning and Deep Learning:

1. Muller & Guido - Machine Learning with Python: https://amzn.to/2Xc0bxS
2. Aurelien Geron - Hands-on Machine Learning: https://amzn.to/2Ri9yqk
3. Ian Goodfellow - Deep Learning: https://amzn.to/31LvHSW
4. Francois Chollet - Deep Learning with Python: https://amzn.to/2KSDvfp

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