PluralSight – Predictive Analytics Using Apache Spark MLlib on Databricks

PluralSight – Predictive Analytics Using Apache Spark MLlib on Databricks Bookware-KNiSO
English | Size: 272.12 MB
Category: Tutorial

This course will teach you to understand and implement important techniques for predictive analytics such as regression and classification using Apache Spark MLlib APIs on Databricks
The Spark unified analytics engine is one of the most popular frameworks for big data analytics and processing. Spark offers extremely comprehensive and easy to use APIs for machine learning which you can use to build predictive models for regression and classification and pre-process data to feed into these models.

In this course, Predictive Analytics Using Apache Spark MLlib on Databricks, you will learn to implement machine learning models using Spark ML APIs. First, you will understand the different Spark libraries available for machine learning, the older RDD-based library, and the newer DataFrame based library. You will then explore the range of transformers available in Spark for pre-processing data for machine learning – such as scaling and standardization transformers for numeric data and label encoding and one-hot encoding transformers for categorical data.

Next, you will use linear regression and ensemble models such as random forest and gradient boosted trees to build regression models. You will use these models for prediction on batch data. In addition, you will also see how you can use Spark ML Pipelines to chain together transformers and estimators to build a complete machine learning workflow.

Finally, you will implement classification models using logistic regression as well as decision trees. You will train the ML model using batch data but perform predictions on streaming data. You will also use hyperparameter tuning and cross-validation to find the best model for your data.

When you’re finished with this course, you’ll have the skills and knowledge to create ML models with Spark MLlib needed to perform predictive analysis using machine learning.

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