• Understand the end-to-end workflow of a Spark ML project.
• Set up the environment by installing Java, Apache Zeppelin, Docker, and Spark.
• Work with Zeppelin notebooks for running Spark jobs and visualizations.
• Understand the house sales dataset and prepare it for machine learning.
• Perform data preprocessing and feature engineering using Spark MLlib.
• Use StringIndexer for handling categorical features.
• Apply VectorAssembler to transform multiple features into a single vector column.
• Split data into training and testing sets for machine learning tasks.
• Train a regression model in Spark MLlib for predicting house sale prices.
• Test and evaluate the regression model with metrics like RMSE.
• Visualize outputs and interpret model results for business insights.
• Run Spark jobs both in Apache Zeppelin and in Databricks (cloud environment).
• Gain practical experience with Spark DataFrames, SQL queries, caching, and job tracking.
• Build confidence to apply Spark MLlib in real-world business projects.

Are you looking to build real-world machine learning projects using Apache Spark?

Do you want to learn how to work with big data, build end-to-end ML pipelines, and apply your skills to a practical use case?

If yes, this course is for you!

In this hands-on project-based course, we will use Apache Spark MLlib to build a House Sale Price Prediction model from scratch. You’ll go beyond theory and actually implement a complete machine learning workflow—covering data ingestion, preprocessing, feature engineering, model training, evaluation, and visualization—all inside Apache Zeppelin notebooks and Databricks.

Whether you are a data engineering beginner, a machine learning enthusiast, or a professional preparing for real-world Spark projects, this course will give you the confidence and skills to apply Spark MLlib to solve real business problems.

What makes this course unique?

• Project-based learning: Instead of just slides, you’ll learn by building an end-to-end project on house price prediction.

• Step-by-step environment setup: We’ll guide you through installing Java, Apache Zeppelin, Docker, and Spark on both Ubuntu and Windows.

• Hands-on with Zeppelin: Learn how to write, run, and visualize Spark code inside Zeppelin notebooks.

• Spark MLlib in action: From RDDs and DataFrames to pipelines and regression models, you’ll gain practical experience in Spark’s machine learning library.

• Performance insights: Learn how to track jobs and optimize performance when working with large datasets.

• Flexible workflow: Work locally with Zeppelin or on the cloud with Databricks free account.

What you’ll work on in the project

• Load and explore a real-world house sales dataset

• Use StringIndexer to handle categorical variables

• Apply VectorAssembler to prepare training data

• Train a regression model in Spark MLlib

• Test and evaluate the model with RMSE (Root Mean Squared Error)

• Visualize and interpret model results for business insights

By the end of the course, you will have built a complete Spark ML project and gained skills you can confidently apply in data science, data engineering, or machine learning roles.

If you want to master Spark MLlib through a real-world project and add an impressive machine learning use case to your portfolio, this course is the perfect place to start!

Who this course is for:

• Data Engineers & Big Data Developers who want to add machine learning with Spark MLlib to their toolkit.
• Data Scientists & ML Engineers who want to run scalable machine learning projects on Spark.
• Students & Beginners who want to learn Spark MLlib through a hands-on, project-based approach.
• Software Developers & Analysts looking to apply Spark for predictive analytics.
• Anyone preparing for interviews in data engineering or Spark-related roles who wants real project experience.
• Professionals who want to enhance their portfolio with a practical machine learning project on house price prediction.