| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Beginner |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹199 / $59 |
| Tools | Python TensorFlow PyTorch Keras Scikit-learn Jupyter Notebook Google Colab Hugging Face |
About the ESG & AI for Sustainable Investing Course
ESG & AI for Sustainable Investing is a comprehensive beginner-level program offered by NanoSchool (NSTC) that provides in-depth training in ESG. The course covers critical areas including AI for Sustainable Investing, equipping learners with both theoretical foundations and practical expertise. Through a carefully structured curriculum, participants will develop the skills needed to tackle real-world challenges in Artificial Intelligence.
Whether you are a student looking to enter the field of Artificial Intelligence, a working professional seeking to upgrade your skill set, or a researcher exploring new methodologies, this course offers a structured learning pathway. Each module combines theoretical concepts with hands-on exercises, case studies, and projects to ensure practical mastery. Upon completion, learners will earn an e-Certification and e-Marksheet from NSTC.
Program Highlights
• Comprehensive coverage of ESG from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Artificial Intelligence
• 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
• Exposure to industry-standard tools and platforms used in Artificial Intelligence
• Career-oriented training for academic and professional growth in Artificial Intelligence
Course Curriculum
Module 1: Introduction to ESG
- Overview and historical evolution of ESG
- Key terminology, definitions, and core concepts in Artificial Intelligence
- Current industry landscape, trends, and career opportunities
- Setting up the learning environment and essential tools
Module 2: Fundamentals and Theoretical Foundations
- Core principles and scientific/theoretical underpinnings of ESG
- Mathematical and analytical frameworks relevant to Artificial Intelligence
- Comparative analysis of major approaches and methodologies
- Understanding key standards, guidelines, and best practices
Module 3: AI for Sustainable Investing
- Core concepts and techniques in AI for Sustainable Investing
- Practical implementation and hands-on exercises
- Integration of AI for Sustainable Investing with ESG workflows
- Case study: Real-world application of AI for Sustainable Investing
Module 4: Neural Networks
- Introduction to Neural Networks concepts and methodologies
- Step-by-step practical implementation of Neural Networks techniques
- Tools and platforms commonly used for Neural Networks
- Troubleshooting, optimization, and best practices
Module 5: Deep Learning
- Introduction to Deep Learning concepts and methodologies
- Step-by-step practical implementation of Deep Learning techniques
- Tools and platforms commonly used for Deep Learning
- Troubleshooting, optimization, and best practices
Module 6: Advanced Topics and Emerging Trends in Artificial Intelligence
- Cutting-edge research and innovations in ESG
- Integration with AI, automation, and modern technologies
- Industry case studies and real-world problem solving
- Future directions and career pathways in Artificial Intelligence
Module 7: Capstone Project and Assessment
- End-to-end project implementation using ESG skills
- Peer review, collaborative exercises, and expert feedback
- Portfolio-ready project documentation and presentation
- Final assessment and course completion evaluation
Tools, Techniques, or Platforms Covered
Python TensorFlow PyTorch Keras Scikit-learn Jupyter Notebook Google Colab Hugging Face
Real-World Applications
- Apply ESG skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Artificial Intelligence competencies
- Solve industry-relevant problems using ESG methodologies and tools
- Contribute to open-source projects and collaborative research in Artificial Intelligence
- Prepare for competitive examinations, interviews, and professional certifications in Artificial Intelligence
Who Should Attend & Prerequisites
- Students pursuing degrees in Artificial Intelligence, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Artificial Intelligence roles
- Researchers and academicians looking to adopt modern techniques in Artificial Intelligence
- Entrepreneurs, freelancers, and self-learners interested in practical Artificial Intelligence knowledge
Certification
