Why this course?

Description

Are you ready to take your Machine Learning and Big Data Analytics skills to the next level?
This hands-on, project-based course is designed to teach you how to build real-world Machine Learning and Analytics projects using Apache Spark 3.0 on Databricks.

Instead of just learning theory, you’ll gain practical, job-ready experience by working on 5 end-to-end projects across multiple domains such as eCommerce, Banking, Shopper Purchase Intent Prediction, Web Analytics, and Predictive Analytics.

Apache Spark has become the industry standard for large-scale data processing and machine learning. With Spark MLlib, you can build scalable models that handle massive datasets efficiently. In this course, you will not only learn how to use Spark MLlib but also get hands-on practice with Regression, Classification, and Predictive Analytics techniques.

By the end of this course, you will be confident in building, training, evaluating, and deploying Spark Machine Learning pipelines—skills that are highly in demand for Data Engineers, Data Scientists, and Machine Learning Engineers.

 

What makes this course unique?

• 5 Real-World Projects: Each section is a complete project covering data preprocessing, model building, evaluation, and interpretation.
• Hands-On with Databricks: Learn how to set up a free Databricks account and run your projects on a real Spark Cluster.
• Step-by-Step Guidance: Even if you’re a beginner, you’ll be guided through every step, from setting up notebooks to building complex ML models.
• Multiple Domains Covered: Projects span eCommerce, Banking, Shopper Intent, Web Analytics, and Predictive Analytics—giving you diverse, practical exposure.
• Focus on Both ML & Analytics: You’ll learn not just predictive modeling, but also how to use Spark for data analytics and insights extraction.

 

Projects You’ll Build

1. eCommerce Project – Build a regression model to solve real-world business problems.
2. Banking Domain Project – Apply machine learning techniques to financial data.
3. Shopper Purchase Intent Prediction – Build classification and regression models to predict customer buying behavior.
4. Web Server Log Analytics Project – Use Spark to analyze massive server log data for insights.
5. Predictive Analytics Project – Implement both classification and regression models using Spark MLlib.

 

By the end of this course, you will be able to:

• Understand the fundamentals of Apache Spark and its MLlib library.
• Work confidently with Spark DataFrames for data preprocessing and transformation.
• Build, train, and evaluate Machine Learning models (Regression & Classification) in Spark.
• Analyze large-scale datasets such as web logs and financial data.
• Apply Spark ML techniques to real-world business problems.
• Run ML projects end-to-end on Databricks Spark clusters.

 

What will students learn in your course?

• Understand the fundamentals of Apache Spark and how it powers large-scale data processing.
• Gain hands-on experience in building 5 real-world projects across different domains
• Projects: eCommerce, Banking, Shopper Purchase Intent, Web Analytics, and Predictive Analytics
• Learn to set up and provision Spark clusters on Databricks for development and experimentation.
• Work with Spark DataFrames for data cleaning, transformation, and feature engineering.
• Build and evaluate machine learning models (Regression & Classification) using Spark MLlib.
• Apply ML concepts like training, testing, evaluation, and model tuning in Spark.
• Perform predictive analytics on structured and unstructured datasets.
• Analyze web server logs with Spark for insights into user behavior and application performance.
• Understand end-to-end project workflows from data ingestion to model deployment.
• Build confidence in applying machine learning and analytics solutions to real-world big data problems.