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DataCamp

Machine Learning with PySpark

via DataCamp

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Overview

Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Spark is a powerful, general purpose tool for working with Big Data. Spark transparently handles the distribution of compute tasks across a cluster. This means that operations are fast, but it also allows you to focus on the analysis rather than worry about technical details. In this course you'll learn how to get data into Spark and then delve into the three fundamental Spark Machine Learning algorithms: Linear Regression, Logistic Regression/Classifiers, and creating pipelines. Along the way you'll analyse a large dataset of flight delays and spam text messages. With this background you'll be ready to harness the power of Spark and apply it on your own Machine Learning projects!

Syllabus

Introduction
-Spark is a framework for working with Big Data. In this chapter you'll cover some background about Spark and Machine Learning. You'll then find out how to connect to Spark using Python and load CSV data.

Classification
-Now that you are familiar with getting data into Spark, you'll move onto building two types of classification model: Decision Trees and Logistic Regression. You'll also find out about a few approaches to data preparation.

Regression
-Next you'll learn to create Linear Regression models. You'll also find out how to augment your data by engineering new predictors as well as a robust approach to selecting only the most relevant predictors.

Ensembles & Pipelines
-Finally you'll learn how to make your models more efficient. You'll find out how to use pipelines to make your code clearer and easier to maintain. Then you'll use cross-validation to better test your models and select good model parameters. Finally you'll dabble in two types of ensemble model.

Taught by

Andrew Collier

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