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The AI Learning Platform for Researchers
250+ expert-curated AI programs built on scientific rigor and real-world applicability — designed to elevate academic researchers, doctoral scholars, and industry R&D professionals.
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Harvard University
Stanford University
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University of Oxford
University of Cambridge
Caltech
Princeton University
Yale University
📘 Researchers • 🎓 PhD Scholars • 🧪 Industry R&D
Courses
Mentors
Participants
Why Researchers Trust NanoSchool AI
Research-Focused Curriculum
Every module is engineered to support publication-quality analysis, reproducible workflows, and high-level academic learning.
Led by Experts
Courses created and taught by seasoned professors, data scientists, and active researchers with real-world domain experience.
Structured Learning Ecosystem
1700+ programs across 27+ tracks, 200+ domains — aligned to scientific advancement and industry relevance.
AI Training Initiatives
Upcoming AI Workshops
AI
AI-Powered Precision Agriculture: Remote Sensing, Satellite Data & Machine Learning for Crop Prediction
🗓 Sep 25, 2026
AI
AI-Powered Structural Biology: AlphaFold-Based Protein Prediction, Validation & Biological Discovery
🗓 Sep 28, 2026
AI
Green Hydrogen 2.0: AI-Driven Electrocatalyst Discovery, Materials Informatics & Techno-Economic Optimization for Hydrogen Production
🗓 Sep 28, 2026
AI
AI-Powered Drug Discovery: Molecular Screening, ADMET Prediction, QSAR Modeling & Lead Optimization
🗓 Sep 28, 2026
AI
Industrial Digital Twins : AI, IoT, Predictive Maintenance & Autonomous Smart Manufacturing
🗓 Sep 30, 2026
AI
AI-Driven Materials Discovery: Machine Learning Potentials, Molecular Dynamics & High-Throughput Screening
🗓 Oct 2, 2026
Course
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AI and Ethics: Governance and Regulation
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Web Development Essentials with AI Integration
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AI for Cybersecurity: Threat Detection and Prevention
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Zinc Finger Nucleases and TALENs: Alternatives to CRISPR
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A Hands-On Program for Genomic Data Analysis
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Hands-On Program for Genomic Data Analysis
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ML Models for Air Quality Prediction and Health Impact
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Greening Campuses: Action-Based Sustainability Implementation
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Learn from Expert Mentors
Connect with industry leaders and academic experts who are passionate about teaching
Dr. M. Prasad
Computer Science and Engineering | Professor | Shri Vishnu Engineering College for Women(A) | 18+ years experience
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About Dr. M. Prasad
Dr. M. Prasad is a Professor in the Department of Computer Science & Engineering at Shri Vishnu Engineering College for Women (Autonomous), Bhimavaram, Andhra Pradesh. With 18+ years of teaching experience, including two years of international teaching in Ethiopia, he has guided 35+ UG/PG projects and currently supervises research scholars. A Google Android Educator, he conducts training on Google products across India and has mentored 150+ Google Certified Student Experts. He has published and presented 45+ research papers, organized 20+ academic events, and is a professional member of bodies including IEEE, ISTE, and CSI. His interests include ML, data mining, cloud, Android (Kotlin), MANETs, security, IoT, and visual analytics.
Areas of Expertise
Professor
Computer Science and Engineering,
Experience & Education
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Testimonials from Researchers & Professionals
very good
Rui Vitorino
Prediction of Immunogenic Response using Orange: A Machine Learning Tool
The mentor talked about the basics of microbial consortium and then explained their applications for bioprocess in detail. The Mentor explained the various topics with a clear and detailed approach.
Anirudh Gupta
Designing and Engineering of Artificial Microbial Consortia (AMC) for Bioprocess: Application Approaches
Very helpful
Priyanka Saha
Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program
The workshop was incredibly insightful, and I truly appreciate the effort you put into creating such a valuable learning experience.
TITIKHYA BARUAH
Biological Sequence Analysis using R Programming
All facilities have explained everything nicely.
Veenu Choudhary
Scientific Paper Writing: Tools and AI for Efficient and Effective Research Communication
Frequently Asked Questions
AI (Artificial Intelligence) refers to systems that perform tasks requiring human-like intelligence — such as learning, reasoning, and decision-making. In AI courses, you typically learn basics of machine learning, neural networks, deep learning, data handling, practical tools, and real-world applications.
Not necessarily. Many beginner-friendly AI courses are designed to be accessible without prior technical experience. However, for advanced workshops, knowledge in Python, statistics, or basic math can be very helpful.
AI workshops vary widely: short hands-on sessions over a few days, multi-week live cohorts, or hybrid models with live sessions and self-paced modules. They can be beginner, intermediate, or advanced in level.
You should gain practical skills such as model building, data analysis, use of AI tools, project experience, and a certificate upon completion. Some programs also support portfolio projects or career guidance.
Yes — most reputable AI workshops include a certificate of completion that can be shared professionally. The recognition depends on the provider’s credibility and your network or industry.
Duration varies: short workshops may run 2–5 days; bootcamps and structured programs may extend over weeks or months with live sessions and projects.
Quality AI workshops typically include live sessions, Q&A, and mentor support — especially for cohort-based formats. Instructor access levels can vary by program.
Most AI programs accept online payments via major gateways (cards, UPI, PayPal) and may offer early-bird or group discounts. Workshops sometimes provide installment or corporate billing options.
Top AI workshops and bootcamps emphasize hands-on learning, practical exercises, and real data projects rather than just lectures or theory.
AI training can open roles such as Data Scientist, ML Engineer, AI Researcher, Business Intelligence Specialist, and domain roles like Predictive Analytics in specific industries — depending on depth and focus.