[Udemy] Employee Attrition Prediction in Apache Spark (ML) Project
What you’ll learn
On this course we’ll implement Spark Machine Studying Project Employee Attrition Prediction in Apache Spark utilizing Databricks Pocket book (Neighborhood server)
Launching Apache Spark Cluster
Course of that information utilizing a Machine Studying mannequin (Spark ML Library)
Discover Apache Spark and Machine Studying on the Databricks platform.
Actual-time Use Case
Apache Spark fundamental and Scala elementary data is required and SQL Fundamentals together with Machine Studying fundamentals
Following browsers on Home windows, Linux or macOS desktop:
Google Chrome (Newest model), Firefox (Newest model), Safari (Newest model), Microsoft Edge* (Newest model)
Web Explorer 11* on Home windows 7, 8, or 10 (with newest Home windows updates utilized)
*You may see efficiency degradation for some options on Microsoft Edge and Web Explorer.
The next browsers should not supported:
Beta, “preview,” or in any other case pre-release variations of desktop browsers.
Spark Machine Studying Project (Employee Attrition Prediction) for inexperienced persons utilizing Databricks Pocket book (Unofficial) (Neighborhood version Server)
On this Knowledge science Machine Studying challenge, we’ll create Employee Attrition Prediction Project utilizing Resolution Tree Classification algorithm one of many predictive fashions.
- Discover Apache Spark and Machine Studying on the Databricks platform.
- Launching Spark Cluster
- Create a Knowledge Pipeline
- Course of that information utilizing a Machine Studying mannequin (Spark ML Library)
- Arms-on studying
- Actual time Use Case
- Publish the Project on Internet to Impress your recruiter
- Graphical Illustration of Knowledge utilizing Databricks pocket book.
- Rework structured information utilizing SparkSQL and DataFrames
Employee Attrition Prediction a Actual time Use Case on Apache Spark
Who this course is for:
- Newbie Apache Spark Developer, Bigdata Engineers or Builders, Software program Developer, Machine Studying Engineer, Knowledge Scientist