Python Exercise for Beginners - One Hot Encoding (Learn Python #3)

#python #pythonforbeginners #pandas #machinelearning #keras #tensorflow #pandas #sklearn

In today's Python exercise we will create a one-hot encoding algorithm to convert categorical data to a numerical format.
The reason why we need to do this is that machine learning algorithms only can deal with numbers, not strings.

I show you first how to create one-hot encodings manually, than for larger categories using NumPy, and then using the most popular data science frameworks, like Keras, Pandas, and Sklearn.

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✅ Timestamps:

0:00 What is One-Hot Encoding
1:29 One-Hot-Encoding for a simple category
1:54 Counting number of elements in category
2:09 Creating a unique words index
3:13 Why Manual One-Hot-Encoding with Binary numbers alone is not feasible
3:56 Web Scraping Genesis chapter from the Bible using requests library
5:07 Cleansing data - removing verse numbers from text
09:26 Generating unique words index for bible text
11:04 One-hot encoding implemented using NumPy
13:28 Setting 1s in the Numpy array
15:54 One-Hot Encoding with Pandas library using get_dummies() function
20:57 Sklearn One-Hot encoding implementation
24:34 One-Hot Encoding with Tensorflow Keras library