Course
Machine Learning with PySpark
AdvancedSkill Level
Updated 11/2025SparkMachine Learning4 hr16 videos56 Exercises4,550 XP29,435Statement of Accomplishment
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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.
Prerequisites
Supervised Learning with scikit-learnIntroduction to PySpark1
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.
2
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.
3
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.
4
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.
Machine Learning with PySpark
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FAQs
Is PySpark good for machine learning?
PySpark offers easy to use and scalable options for machine learning tasks for people who want to work in Python. You can work on distributed systems, and use machine learning algorithms and utilities, such as regression and classification thanks to the MLlib. It’s a great option for people who want to build machine learning pipelines and are already familiar with Python libraries such as pandas.
Is this course suitable for beginners?
This course is not suitable for complete beginners to PySpark. We recommend that you take our Introduction to PySpark and Supervised Learning with scikit-learn in order to fully benefit from the course and gain an introduction to both elements of the course.
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