Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
2 Hour(s) 59 Minute(s)
Language
English
Taught by
Bigdata Engineer
Rating
1 views
Course Overview
Disease Prediction 2 Projects in Apache Spark(ML) for beginners using Databricks Notebook (Unofficial) Community edition
Heart Attack and Diabetes Prediction Project in Apache Spark
Are you curious about how Big Data and Machine Learning can be applied to solve real-world healthcare problems?
Do you want to learn how to use Apache Spark to build end-to-end prediction projects for critical conditions like heart disease and diabetes?
This project-based course is designed to give you hands-on experience in applying Apache Spark with Machine Learning to build predictive models that can analyze patient health data and predict the likelihood of disease.
You won’t just learn theory — you’ll work step by step on two real-world healthcare prediction projects:
Heart Attack Prediction Project
Diabetes Prediction Project
By the end of the course, you will have the practical knowledge to ingest, process, and analyze medical data at scale using Spark, and build predictive models that can be applied to real-life scenarios.
What makes this course unique?
Hands-on Projects – You will build two healthcare prediction projects from scratch.
Step-by-step Guidance – From Spark basics to advanced ML modeling.
Industry-Relevant Skills – Learn how Spark is applied to healthcare and big data analytics.
Databricks Environment – You’ll get free access to Databricks to run Spark projects without complex installations.
What’s inside the course?
Section 1 & 2: Getting Started
Introduction, downloading resources, and environment setup on Databricks.
Section 3: Project Basics
Learn Apache Spark fundamentals, creating clusters, working with notebooks, DataFrames, and basics of Machine Learning.
Section 4: Heart Attack Prediction Project
Build your first Spark ML project step by step: data preprocessing, model building, evaluation, and predictions.
Section 5: Diabetes Prediction Project
Apply your skills to another real-world healthcare dataset and build a prediction model for diabetes.
By the end of this course, you will:
Understand how to use Apache Spark for Machine Learning projects.
Build real-world prediction models for healthcare datasets.
Get hands-on practice with Spark DataFrames, ML pipelines, and model evaluation.
Use Databricks to create and manage Spark clusters for project execution.
Gain the confidence to apply Spark in other domains such as finance, retail, and telecom.
This is a perfect project-based course if you want to strengthen your Spark + ML skills and also work on impactful healthcare problems.
Course Content
- 5 section(s)
- 21 lecture(s)
- Section 1 Introduction
- Section 2 Download Resources
- Section 3 Project Basics
- Section 4 Heart Disease Prediction Project
- Section 5 Diabetes Prediction Project
What You’ll Learn
- Understand the fundamentals of Apache Spark and its role in Big Data and Machine Learning.
- Learn how to set up and run Spark clusters in Databricks (free cloud environment).
- Work with Spark DataFrames for healthcare datasets and perform data preprocessing.
- Build an end-to-end Heart Disease Prediction Project using Spark ML.
- Build an end-to-end Diabetes Prediction Project using Spark ML.
- Apply Machine Learning techniques like feature engineering, model training, and evaluation in Spark.
- Learn to use notebooks effectively for data exploration, analysis, and documentation.
- Understand how to deploy and interpret ML models in real-world healthcare contexts.
- Develop confidence to apply Spark ML techniques to other domains (finance, telecom, retail, etc.).
Reviews
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MMOMINUL HOQUE
GOOD
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SSNEHALATHA SALLUNKHE
great information.
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SSanjay Prakash Chavan
Good
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AAlberto Scalise
Useful introduction to Databricks (although some screenshots should be updated to the new version). I think the Project Explanation sections should be improved, for example when the choices made on the Decision Tree Classifier or Logistic Regression algorithms are not justified (or they are taken for granted).
