| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 4 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $82 |
| Tools | Apache Hadoop HDFS MapReduce YARN TensorFlow PyTorch |
About the Apache Hadoop Basics Course
This program explores how Apache Hadoop's robust ecosystem can be utilized to support advanced AI functionalities, focusing on the integration of big data technologies with AI tools to enhance data processing and analysis capabilities.
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Program Highlights
• Comprehensive coverage of Apache Hadoop Basics from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Big Data
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Apache Hadoop, HDFS, MapReduce, YARN
• Career-oriented training for academic and professional growth in Big Data
Course Curriculum
Module 1: Module 1 – Introduction to Hadoop and AI
- Understand the fundamentals of Hadoop and its core components
- Explore AI concepts and their synergy with Hadoop
- Identify use‑cases where big data fuels intelligent solutions
Module 2: Module 2 – Setting Up Hadoop for AI
- Install and configure Hadoop clusters on cloud or on‑premise
- Integrate popular AI libraries (TensorFlow, PyTorch) with Hadoop
- Validate the environment with sample AI workloads
Module 3: Module 3 – Data Management in Hadoop
- Store massive datasets efficiently using HDFS
- Process data at scale with MapReduce jobs
- Optimize data pipelines for AI model training
Module 4: Module 4 – AI Models with Hadoop
- Develop machine‑learning models using Hadoop‑based frameworks
- Deploy models across the cluster for distributed inference
- Monitor performance and iterate on model improvements
Module 5: Module 5 – Scalability and Performance
- Scale AI applications horizontally across nodes
- Tune Hadoop parameters for maximum throughput
- Implement best practices for fault‑tolerant AI workloads
Module 6: Module 6 – Project and Real‑world Applications
- Plan and execute a capstone AI project on Hadoop
- Explore industry case studies across finance, healthcare, and retail
- Present solutions and receive expert feedback
Tools, Techniques, or Platforms Covered
Apache Hadoop HDFS MapReduce YARN TensorFlow PyTorch
Real-World Applications
- Apply Apache Hadoop Basics skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Big Data competencies
- Solve industry-relevant problems using Apache Hadoop Basics methodologies and tools
- Contribute to open-source projects and collaborative research in Big Data
- Prepare for competitive examinations, interviews, and professional certifications in Big Data
Who Should Attend & Prerequisites
- Students pursuing degrees in Big Data, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Big Data roles
- Researchers and academicians looking to adopt modern techniques in Big Data
- Entrepreneurs, freelancers, and self-learners interested in practical Big Data knowledge
Certification
