FeaturedUdemy
Machine Learning with PySpark
4.8(710)29k studentsADVANCEDEnglish
Korshub's take
The Korshub Take
This advanced course teaches you how to scale machine learning algorithms to massive datasets using Apache Spark and PySpark. It covers Spark's distributed compute capabilities, ML pipelines, and key classification and regression algorithms.
Who It's For
Data engineers and data scientists looking to build big data machine learning models and pipelines across distributed clusters.
Key Takeaways
- Distributed ML Pipelines: Ingest big data into Spark and build end-to-end Machine Learning pipelines.
- Decision Trees & Recursive Partitioning: Implement decision trees using recursive partitioning to split data and evaluate node split information.
- Spark Regressors & Classifiers: Master PySpark implementations of Linear Regression and Logistic Regression to refine predictive modeling.
About this course
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.
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