Turn Your Ideas into Impact
Research Intake Program 2026

A 4-month intensive training program designed to transform you from a student into a published researcher in AI and Robotics.

Apply for Intake 2026 Download Brochure
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176+ Hours
of Live Training
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10+
Publications by UG Students
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Top Students
Qualify for Internship
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Expert
Mentorship from PhDs & Industry Leaders

Transform Your Career with Hands–On Research

This intensive 4-month program is designed for aspiring researchers, B.Tech students, and professionals who want to bridge the gap between theoretical knowledge and real-world application. Our curriculum takes you from the fundamentals of Python to advanced concepts in AI and research methodology.

The ultimate goal: to equip you with the skills to develop your own project, write a high-quality research paper, and get it published in a reputable journal or conference.

A Deep Dive into the Curriculum

A Deep Dive into the Curriculum

Our 4-month program is meticulously structured to take you from foundational concepts to advanced specializations, culminating in a publishable research project.

This foundational month is designed to build a robust base in machine learning and artificial intelligence. You'll master essential supervised and unsupervised learning techniques, progress to building and training deep neural networks, and dive deep into the world of NLP, from text preprocessing to state-of-the-art transformer models. By the end, you’ll have hands-on experience with real data and the confidence to tackle advanced AI projects.
Approx. 44 Live Hours | 4 Mini-Projects
Module 1: Machine Learning Foundations
Duration: 18 Hours (Tutorial: 15 hrs | Practical: 2 hrs | Reading: 1 hr)
Week 1–2: Core Machine Learning Concepts
  • Day 1: Introduction to Machine Learning (3 hrs)
  • Day 2: Data Preprocessing & Feature Engineering (3 hrs)
  • Day 3: Supervised Learning: Regression (3 hrs)
  • Day 4: Supervised Learning: Classification (3 hrs)
  • Day 5: Ensemble Methods (3 hrs)
  • Day 6: Unsupervised Learning (3 hrs)
Module 2: Deep Learning Fundamentals
Duration: 12 Hours (Tutorial: 9 hrs | Practical: 2 hrs | Reading: 1 hr)
Week 3: Neural Networks Foundation
  • Day 7: Introduction to Neural Networks (3 hrs)
  • Day 8: Training Deep Networks (3 hrs)
  • Day 9: Advanced Deep Learning & Transfer Learning (3 hrs)
Module 3: Natural Language Processing
Duration: 48 Hours (Tutorial: 36 hrs | Practical: 8 hrs | Reading: 4 hrs)
Week 4–7: NLP Fundamentals to Advanced
  • Day 10: NLP Foundations (3 hrs)
  • Day 11: Text Preprocessing & Feature Engineering (3 hrs)
  • Day 12: Introduction to Text Classification (3 hrs)
  • Day 13: Word Embeddings & Vector Representations (3 hrs)
  • Day 14: Sentence & Document Representation (3 hrs)
  • Day 15: Named Entity Recognition & POS Tagging (3 hrs)
  • Day 16: Recurrent Neural Networks (RNNs) (3 hrs)
  • Day 17: Seq2Seq Models & Attention Mechanism (3 hrs)
  • Day 18: Understanding Transformers & Self-Attention (3 hrs)
  • Day 19: BERT, RoBERTa, ALBERT & Applications (3 hrs)
  • Day 20: Fine-Tuning Language Models (3 hrs)
  • Day 21: Advanced Text Generation Using GPT & LLaMA (3 hrs)
  • Day 22: Sentiment Analysis Fundamentals (3 hrs)
  • Day 23: ABSA & Emotion Recognition (3 hrs)
  • Day 24: Semantic Analysis & Text Similarity (3 hrs)
  • Day 25: Chatbots & Dialogue Systems (3 hrs)
  • Understanding and applying core machine learning algorithms (regression, classification, clustering, ensemble methods)
  • Data preprocessing, feature engineering, and analysis
  • Designing, training, and tuning deep neural networks
  • Transferring learning and implementing advanced architectures
  • NLP pipelines: word embeddings, RNNs, transformers, sentiment analysis, and chatbots
  • Evaluating model performance and interpreting results
Numpy Pandas Matplotlib Seaborn Scikit-learn NLTK
This month immerses you in the field of computer vision, taking you from the fundamentals of image processing and feature extraction to advanced deep learning for visual understanding. You'll master core topics like CNNs, object detection, segmentation, generative models, and the latest in vision transformers and vision-language models. By the end, you’ll be able to build, analyze, and innovate cutting-edge vision systems used in modern AI applications
Approx. 44 Live Hours | 4 Mini-Projects
Module 4: Computer Vision Fundamentals
Duration: 36 Hours (Tutorial: 27 hrs | Practical: 6 hrs | Reading: 3 hrs)
Week 8–10: Computer Vision from Basics to Advanced
  • Day 26: Understanding Computer Vision (3 hrs)
  • Day 27: Image Preprocessing Techniques (3 hrs)
  • Day 28: Feature Detection & Keypoint Matching (3 hrs)
  • Day 29: Introduction to CNNs (3 hrs)
  • Day 30: Advanced CNN Architectures (3 hrs)
  • Day 31: Object Detection Fundamentals (3 hrs)
  • Day 32: Advanced Object Detection & Tracking (3 hrs)
  • Day 33: Image Segmentation (3 hrs)
  • Day 34: Generative Models - GANs (3 hrs)
  • Day 35: Diffusion Models & Image Generation (3 hrs)
  • Day 36: 3D Vision & Video Processing (3 hrs)
  • Day 37: Explainable AI & Medical Imaging (3 hrs)
Module 5: Vision Transformers & Vision-Language Models
Duration: 20 Hours (Tutorial: 15 hrs | Practical: 4 hrs | Reading: 1 hr)
Week 11–12: Cutting-Edge Multi-Modal AI
  • Day 38: Introduction to Vision Transformers (ViT) - Part 1 (4 hrs)
  • Day 39: Advanced Vision Transformer Architectures (4 hrs)
  • Day 40: Introduction to Vision-Language Models (VLMs) (4 hrs)
  • Day 41: CLIP & Contrastive Vision-Language Learning (4 hrs)
  • Day 42: Advanced VLMs & Real-World Applications (4 hrs)
  • Image preprocessing, enhancement, and classical feature extraction
  • Designing, training, and evaluating convolutional neural networks
  • Implementing advanced architectures for object detection and segmentation
  • Applying generative models (GANs, diffusion) and explainable AI in vision
  • Leveraging vision transformers (ViTs) and vision-language models (VLMs)
  • Integrating multimodal techniques for cutting-edge AI solutions
TensorFlow PyTorch Keras OpenCV HuggingFace SHAP LIME
This month covers advanced, cutting-edge topics in computer vision and AI research tools essential for state-of-the-art machine learning workflows. Topics range from self-supervised learning and multi-modal AI to edge AI, robotics, and integration with emerging tools like Ollama and DeepSeek. This prepares you to stay ahead in rapidly evolving AI research, design efficient models, and apply ethical and responsible AI principles in real-world applications.
Approx. 44 Live Hours | 3 Mini-Projects
Module 6: Advanced Topics & Research Tools
Duration: 36 Hours (Tutorial: 27 hrs | Practical: 6 hrs | Reading: 3 hrs)
Week 13–14: Specialized Topics
  • Day 43: Self-Supervised Learning for Vision (3 hrs)
  • Day 44: Multi-Modal Learning Beyond VLMs (3 hrs)
  • Day 45: Edge AI & Model Optimization (3 hrs)
  • Day 46: Autonomous Systems & Robotics CV (3 hrs)
  • Day 47: Introduction to Ollama (3 hrs)
  • Day 48: Working with Ollama APIs (3 hrs)
  • Day 49: Introduction to DeepSeek (3 hrs)
  • Day 50: Advanced Tool Integration (3 hrs)
  • Day 51: Latest Research Trends in NLP (3 hrs)
  • Day 52: Latest Research Trends in CV (3 hrs)
  • Day 53: Industry Applications & Case Studies (3 hrs)
  • Day 54: Ethics, Bias & Responsible AI (3 hrs)
  • Implementing self-supervised learning techniques for vision tasks
  • Developing multi-modal AI systems leveraging diverse data types
  • Optimizing models for edge deployment and real-time applications
  • Building autonomous systems and robotics vision pipelines
  • Utilizing novel research tools including Ollama and DeepSeek platforms
  • Tracking latest developments in NLP and CV research
  • Applying ethical AI practices and bias mitigation in AI systems
Ollama DeepSeek GoogleAi OpenAI Meta
This month focuses on equipping you with the essential skills and methodologies required to conduct high-quality AI research and successfully write scientific papers. You will learn the full research cycle from formulating research problems, conducting literature reviews, designing experiments, collecting and analyzing data, to structuring and writing research papers using the IMRaD format. Ethical considerations and publishing practices ensure responsible and impactful scientific contributions
Approx. 44 Live Hours | 3 Mini-Projects + Capstone
Module 7: Research Methodology & Paper Writing
Duration: 30 Hours (Tutorial: 21 hrs | Practical: 6 hrs | Reading: 3 hrs)
Week 15–16: Research Skills Development
  • Day 55: Fundamentals of Research (3 hrs)
  • Day 56: Research Problem Identification (3 hrs)
  • Day 57: Literature Review Techniques (3 hrs)
  • Day 58: Research Design & Methodology (3 hrs)
  • Day 59: Data Collection & Analysis (3 hrs)
  • Day 60: Research Paper Structure – IMRaD (3 hrs)
  • Day 61: Writing Methods & Results Sections (3 hrs)
  • Day 62: Formatting & Citation Styles (3 hrs)
  • Day 63: Publishing & Conference Submissions (3 hrs)
  • Day 64: Research Ethics & Peer Review (3 hrs)
  • Understanding fundamentals of AI research and formulation of research questions
  • Designing sound experimental methodologies and data analysis techniques
  • Effective literature review and critical evaluation of scientific works
  • Structuring and writing research papers, including methods, results, and discussion
  • Mastery of formatting, citation styles, and ethical standards in AI research
  • Navigating the publishing process, conference submissions, and peer review
LaTeX Mendeley YOLO IEEE Scopus Google-Scholar

Student Achievements

Discover the real-world impact our students have made through their research and publications.

PK

Pakhi Kansal

1st Position

"Secured 1st Position with 2 high-impact papers, including a Springer Nature publication and a Best Paper Award at ICUS 2024."

View Publication →
JS

Jyoti Sharma

2nd Position

"Secured 2nd Position, co-authoring award-winning research on NLP-based systems recognized at ICUS 2024."

View Publication →
KT

Khushi Thakur

3rd Position

"Secured 3rd Position with an IEEE conference publication on railway automation."

View Publication →

Exclusive Benefits

Live Lectures (176+ Hours)

Doubt Solving Sessions

50+ Research Ideas Overview

Full Publication Support

5 Research Proposals

Opensource Research Tools

Daily Class PDFs & .ipynb Files

120+ Project Dev Hours

Your Pathway to a UG Research Internship

1

4-Months Training

Complete the intensive training in AI, CV, NLP, and Research Methodology.

2

Top Most Selection

Excel in the program and secure your position in the top 20% of the cohort.

3

8-Month UG Research Internship

Receive an official Offer Letter and work on Gudsky's real-world R&D projects.

Pricing & Registration

Choose your plan and start your research journey. Limited seats available.

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Early Bird Offer

15 Oct - 15 Nov 2025
₹ 14,199 + GST
₹ 51,599
Apply Now

Regular Price

16 Nov - 31 Dec 2025
₹ 19,999 + GST
₹ 51,599
Apply Now

Registration closes on 31 Dec 2025.

Apply for the Research Intake 2026 Cohort