Iām Adarsh Pandey, an MCA student specializing in Artificial Intelligence & Data Science at Graphic Era University, exploring Machine Learning, Deep Learning, Generative AI and research-driven problem solving.
Bridging mathematical foundations with research-driven AI systems.
Iām Adarsh Pandey, currently pursuing an MCA in Artificial Intelligence & Data Science at Graphic Era University.
My journey into technology started with a BCA at Microtech College of Management and Technology, where I developed my foundation in programming and computer science principles. I am now focusing on Artificial Intelligence, Machine Learning, Data Science, and research-oriented problem solving.
My long-term goal is to become an AI Researcher / AI Research Scientist and contribute to meaningful, foundational advancements in Artificial Intelligence.
I believe strong AI requires more than using existing models. It requires understanding mathematics, algorithms, experimentation, implementation, and the rigorous research behind modern intelligent systems.
MCA ā AI & Data Science
Graphic Era UniversityArtificial Intelligence & Research
Algorithms & Empirical TestingAI Research Scientist
Building Foundational IntelligenceHonest proficiency assessment focused on continuous learning and technical mastery.
Academic institutions shaping my computer applications, AI, and data science specialization.
Specialized Master's curriculum focused on Machine Learning, Deep Learning, Data Analytics, Neural Networks, and AI Research methodology.
Undergraduate foundation covering programming paradigms, data structures, database management systems, software engineering, and computer science fundamentals.
Verified technical coursework and ongoing specialized learning certifications.
Core Python programming, data manipulation with NumPy/Pandas, visualization, and data analysis fundamentals.
ā Verified LearningSupervised regression, classification algorithms, model evaluation metrics, and feature selection pipelines.
ā Verified LearningExploratory coursework on perceptrons, backpropagation gradients, PyTorch fundamentals, and neural architectures.
ā³ Active CourseworkRelational database design, complex SQL queries, indexing, schema normalization, and JSON data management.
ā Verified LearningExploring key domains shaping the future of intelligent systems.
Investigating core algorithmic principles, decision systems, and intelligent agent architectures.
Studying supervised, unsupervised algorithms, model optimization, and generalization techniques.
Exploring neural network dynamics, deep backpropagation, representation learning, and PyTorch/TensorFlow.
Exploring generative modeling paradigms, latent space representations, and synthesis algorithms.
Analyzing Transformer attention mechanisms, sequence modeling, prompting, and token embeddings.
Investigating visual feature extraction, image classification, object detection, and spatial understanding.
Studying semantic parsing, sentiment analysis, language comprehension, and textual embeddings.
Examining model robustness, ethical alignment, bias mitigation, and interpretability in AI models.
Technical implementations combining web architecture, data science, and AI matching concepts.
A digital platform designed to help users explore the cultural, spiritual and historical heritage of Varanasi.
A social-impact concept designed to reduce food waste by intelligently connecting restaurants with NGOs that distribute surplus food.
A data science and machine learning project exploring predictive analytics and statistical trend modeling on stock market data.
A programming project implementing fundamental banking operations, customer management, security controls, and JSON data handling.
A structured progression from foundational computing to AI research scientist ambition.
Developed strong core computer science fundamentals, data structures, and object-oriented programming at Microtech College.
Enrolled at Graphic Era University specializing in Artificial Intelligence and Data Science coursework.
Deepening knowledge in Linear Algebra, Multivariate Calculus, Probability, and Statistical Inference required for AI theory.
Mastering supervised and unsupervised learning algorithms, feature engineering, and model evaluation metrics.
Understanding backpropagation, convolutional layers, recurrent architectures, PyTorch, and TensorFlow.
Exploring Transformer architectures, self-attention, prompt engineering, and generative model dynamics.
Reading seminal AI literature, reproducing experimental setups, and questioning algorithmic assumptions.
Contributing novel insights, publishing research, and designing next-generation intelligent systems.
Sequential milestone map detailing the technical progression toward AI research capability.
An authentic documentation hub for ongoing experiments, paper notes, and model implementations.
Empirical evaluations comparing optimization algorithms (Adam, SGD, AdamW) across image classification datasets.
Detailed reading notes on "Attention Is All You Need" (Vaswani et al.) and foundational self-attention mechanics.
Building a mini deep learning automatic differentiation engine in pure Python/NumPy for learning backpropagation.
Preprocessed time-series and textual datasets prepared for machine learning feature engineering tasks.
Exploratory small-scale model fine-tuning experiments focused on domain-specific NLP queries.
Conceptual notes examining safe agent decision boundaries and verifiable constraints in autonomous planning.
Live profile integration tracking continuous coding and repository developments.
Documenting my code, research experiments, and computer science projects transparently on GitHub.
Core subjects and technical areas undergoing active study and practice.
Python for AI/ML
Data Structures & Algorithms
Statistics & Inference
Probability Theory
Linear Algebra
Machine Learning
Deep Learning
Generative AI
Research Methodology
AI Research Papers
AI is not just about using models. It is about understanding, experimenting and discovering what comes next.