This is the overview page for the Machine Learning Tutorial. This tutorial should give you a quick overview of Machine Learning. This tutorial won’t require any coding skills, we won’t use R or Python in this tutorial (yet). At the end of this tutorial, you can find a link to other tutorials that take this into consideration. The purpose of this tutorial is to understand the basics, so that you are prepared when working with Spark and Python!

Machine Learning Tutorial contents

  1. Intro: supervised and unsupervised Machine Learning
  2. Clustering, Regression and Classification
  3. The Linear Regression: how it is done
  4. Calculating the Prediction Error and Standard Error in a Linear Regression
  5. Lift and Gain to measure the performance of a model
  6. False positives and False negatives
  7. An introduction to the logistic regression
  8. Classification algorithms: Random Forest and Naive Bayes
  9. What is Deep Learning?

The next step after completing this tutorial is to learn about Apache Spark in this tutorial or to learn about Python in this tutorial. If you want to learn more about Data Science and Data Engineering, Have a look at the other Tutorials on it.

If you want to learn everything about Apache Spark, make sure to visit the Spark Website.

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A linear regression model
A linear regression model