| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2749 / $29 |
| Tools | Python TensorFlow PyTorch Google Colab CNN SVM YOLOv10 Spectrogram Analysis SAR Optical Fusion |
About the ML for Ocean Health: Monitoring Marine Ecosystems with AI Course
ML for Ocean Health: Monitoring Marine Ecosystems with AI explores how artificial intelligence and machine learning can transform ocean monitoring and conservation.
Participants will learn to analyze satellite, acoustic, and underwater imaging data to track biodiversity, pollution, and ecosystem changes.
Program Highlights
• Comprehensive coverage of ML for Ocean Health from fundamentals to advanced applications
• Hands-on projects and real-world case studies in environmental-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: Python, TensorFlow, PyTorch, Google Colab
• Career-oriented training for academic and professional growth in environmental-ai
Course Curriculum
Module 1: Bio‑Indicators & Computer Vision
- Develop coral health classification pipelines using hybrid CNN‑SVM models.
- Implement real‑time fish species identification and counting with YOLOv10.
- Analyze acoustic soundscapes via spectrogram‑based deep learning to separate biophony from anthropophony.
Module 2: Pollution Tracking & Habitat Stress
- Fuse SAR and optical satellite data to detect oil spills and chemical runoff.
- Forecast harmful algal blooms with LSTM models using SST and chlorophyll‑a.
- Segment mangrove and seagrass habitats to quantify blue‑carbon sequestration.
Module 3: Conservation Strategy & Policy AI
- Design reinforcement‑learning agents to optimize Marine Protected Area boundaries.
- Detect illegal fishing activities using AIS trajectory analysis.
- Apply XAI (SHAP) to explain priority zones for coastal restoration.
Tools, Techniques, or Platforms Covered
Python TensorFlow PyTorch Google Colab CNN SVM YOLOv10 Spectrogram Analysis SAR Optical Fusion
Real-World Applications
- Apply ML for Ocean Health skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical environmental-ai competencies
- Solve industry-relevant problems using ML for Ocean Health methodologies and tools
- Contribute to open-source projects and collaborative research in environmental-ai
- Prepare for competitive examinations, interviews, and professional certifications in environmental-ai
Who Should Attend & Prerequisites
- Students pursuing degrees in environmental-ai, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into environmental-ai roles
- Researchers and academicians looking to adopt modern techniques in environmental-ai
- Entrepreneurs, freelancers, and self-learners interested in practical environmental-ai knowledge
Certification
