Data Preparation for Machine Learning Algorithms | Complete Tutorial

In this video, we will cover how to prepare and process your data for machine learning. We will cover the following:
- Dealing with missing data
- Imputing data with the mean
- Dropping rows with missing data
- Standardizing your data
- Encoding independent variables (One Hot Encoding)

These methods will be used for pretty much any machine learning algorithm you can think of including, but not limited to regression analysis, logistic regression, KNN (k-nearest-neighbor), K-means clustering, support vector machines (SVM) and so much more!

Revernos is a technology channel that covers a wide range of topics including, but not limited to, machine learning, cloud computing (Amazon Web Services), programming (Python), financial engineering, physical computing (Raspberry Pi), artificial intelligence, data analytics, and more!

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