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Machine Learning with PySpark

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

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4 Hours16 Videos56 Exercises17,273 Learners4550 XPBig Data with PySpark TrackMachine Learning Scientist Track

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Course Description

Learn to Use Apache Spark for Machine Learning

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.

Build and Test Decision Trees

Building your own decision trees is a great way to start exploring machine learning models. You’ll use an algorithm called ‘Recursive Partitioning’ to divide data into two classes and find a predictor within your data that results in the most informative split of the two classes, and repeat this action with further nodes. You can then use your decision tree to make predictions with new data.

Master Logistic and Linear Regression in PySpark

Logistic and linear regression are essential machine learning techniques that are supported by PySpark. You’ll learn to build and evaluate logistic regression models, before moving on to creating linear regression models to help you refine your predictors to only the most relevant options.

By the end of the course, you’ll feel confident in applying your new-found machine learning knowledge, thanks to hands-on tasks and practice data sets found throughout the course.
  1. 1

    Introduction

    Free

    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.

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    Machine Learning & Spark
    50 xp
    Characteristics of Spark
    50 xp
    Components in a Spark Cluster
    50 xp
    Connecting to Spark
    50 xp
    Location of Spark master
    50 xp
    Creating a SparkSession
    100 xp
    Loading Data
    50 xp
    Loading flights data
    100 xp
    Loading SMS spam data
    100 xp

In the following tracks

Big Data with PySparkMachine Learning Scientist

Datasets

FlightsSMS

Collaborators

hadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9
Hadrien Lacroix
mona-kay
Mona Khalil
Andrew Collier Headshot

Andrew Collier

Data Scientist @ Exegetic Analytics

Andrew Collier is a Data Scientist, working mostly in R and Python but also dabbling in a wide range of other technologies. When not in front of a computer he spends time with his family and runs obsessively.
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