| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 4 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $82 |
| Tools | Apache Spark MLlib TensorFlow PyTorch Structured Streaming Hadoop |
About the Apache Spark Basics Course
This course provides an in-depth exploration of Apache Spark, its core concepts, and its integration with AI technologies to enhance machine learning capabilities and handle big data with ease.
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Program Highlights
• Comprehensive coverage of Apache Spark Basics from fundamentals to advanced applications
• Hands-on projects and real-world case studies in AI
• 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 Spark, MLlib, TensorFlow, PyTorch
• Career-oriented training for academic and professional growth in AI
Course Curriculum
Module 1: Introduction to Apache Spark
- Understand Spark ecosystem and advantages over traditional frameworks
- Explore core components and architecture
- Set up Spark environment for hands‑on labs
Module 2: Spark Core Concepts
- Manipulate data using RDDs and DataFrames
- Write efficient transformations and actions
- Optimize performance with caching and partitioning
Module 3: Spark for AI Applications
- Apply MLlib algorithms to build machine‑learning models
- Integrate Spark with TensorFlow and PyTorch
- Deploy scalable AI pipelines on Spark clusters
Module 4: Advanced Data Processing
- Implement real‑time stream processing with Structured Streaming
- Handle massive datasets using Spark SQL and Catalyst optimizer
- Tune jobs for high‑throughput AI workloads
Module 5: Real‑World Applications & Case Studies
- Explore Spark use‑cases in healthcare, finance, and e‑commerce
- Analyze performance tuning techniques
- Adopt best practices for production AI systems
Module 6: Capstone Project
- Design an end‑to‑end Spark AI solution
- Implement data ingestion, model training, and deployment
- Present findings and receive expert feedback
Tools, Techniques, or Platforms Covered
Apache Spark MLlib TensorFlow PyTorch Structured Streaming Hadoop
Real-World Applications
- Apply Apache Spark Basics skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical AI competencies
- Solve industry-relevant problems using Apache Spark Basics methodologies and tools
- Contribute to open-source projects and collaborative research in AI
- Prepare for competitive examinations, interviews, and professional certifications in AI
Who Should Attend & Prerequisites
- Industry‑recognized e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
