🚂 AI ✈ ML 🛫 Core 🛳 Foundations 🚁 is a 🧸 comprehensive 🚋 that 🎁 provides 🚞 a solid 🚀 starting point 🛩 Artificial 🚟 Intelligence 🛸 and Machine 🚝 Learning 🚤 It covers 🏛 the ML 🏘 algorithms 🏬 mathematical 🕌 foundations 🏦 implementations 🏣 helping 🧱 learners 🚌 transition ⚽ from ⚾ theory to 🥎 hands on 🏀 projects 🏐 with 🏆 ease 🎳

I completed the Alan Turing Institute’s Mathematics for Machine Learning – Summer School, exploring the core mathematical foundations of modern AI and ML.

A collection of hands-on Jupyter notebooks covering essential Python libraries for Data Science and Machine Learning — including NumPy, pandas, Matplotlib, Seaborn, Scikit-learn, PyTorch, and TensorFlow. Each notebook demonstrates core concepts, real-world examples, and practical use cases to build strong foundations for ML and AI projects.

A collection of AI models implemented from scratch using pure NumPy, focusing on core mathematical foundations without high-level ML libraries.

This repository covers core Python concepts, mathematical foundations for AI/ML, and small-scale projects. It includes clear explanations, practical examples, and implementations using frameworks like NumPy, Pandas, and Matplotlib. Contributions and feedback are welcome as I progress in my learning journey.

This 5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation. Finally, you’ll discover how agents move beyond LLM prototypes to become production-ready systems.

Repository for the AI/ML Fellowship (GDGOC-Atk) Week 1 tasks, covering Python foundations, Git/GitHub professional workflows, and core AI/ML/DL concepts.

Core Python foundations for Data Science & AI. Covers Data Structures, Algorithms, and Mathematics for ML (NumPy/Pandas implementation).

Python practice repository covering core concepts, problem-solving, and interview questions. Focused on building strong foundations for AI/ML engineering.

Oracle Cloud Infrastructure AI Foundations provides core AI and ML services, including data management, prebuilt AI APIs, and generative AI, to help build, deploy, and scale intelligent applications on OCI.

My own challenge, create a portfolio of AI & Math projects (12 projects) showcasing mastery of core ML concepts, mathematical foundations, and applied research experiments.

Structured Python notes for AI/ML foundations — covers core Python, NumPy, Pandas, Matplotlib, Sklearn, and ML workflows. Built as a personal reference with inline explanations, topic-wise files, and complexity notes. (B.Tech CSE | AI & Data Science).

Structured Python notes for AI/ML foundations — covers core Python, NumPy, Pandas, Matplotlib, Sklearn, and ML workflows. Built as a personal reference with inline explanations, topic-wise files, and complexity notes. (B.Tech CSE | AI & Data Science).

A structured 6-stage roadmap to learn AI & ML from scratch to intermediate level. Covers Python & math foundations, core ML algorithms, model evaluation, deep learning basics, advanced topics, and capstone projects. Includes examples, practice, and projects for building a strong portfolio.

This course covers core ML and state-of-the-art AI techniques, including self-supervised learning, foundation models, and NLP. It combines theoretical foundations with practical implementations, focusing on experiment design, responsible AI, medical applications, and model optimization for modern AI systems.

ML Research Companion is my curated log of research papers in Machine Learning. It progresses from core foundations (optimization, CNNs, transformers) to practical frameworks (TensorFlow, PyTorch, ONNX) and advanced ML systems (deployment, edge AI, scalability), with concise notes, key ideas, and insights.

5-Day AI Agents Intensive course with Google. as crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. It covers the core – models, tools, orchestration, memory and evaluation.

This online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. It includes the core components – models, tools, orchestration, memory and evaluation.

A JARVIS-style AI virtual assistant built using Python and modern ML/NLP techniques. Focused on text processing, embeddings, and model training as core foundations. Designed to evolve into a hybrid offline–online intelligent automation system.

This 5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation.

This 5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation.

Day-1 ML foundations focused on linear algebra: vectors, dot products, norms, angles, projections, and basic matrix operations—implemented in a Jupyter notebook with NumPy/Matplotlib, clear 2D visuals, a self-quiz, and a brief reflection. Emphasizes how these math primitives map to core ML/AI ideas (similarity, least squares, geometric transforms).

Software Engineer with strong foundations in Data Structures, Algorithms, and Core Computer Science. M.Tech CSE @ NIT Durgapur | GATE CSE 2023 – AIR < 1000 Experience in full-stack development and AI/ML-based systems, with a focus on clean architecture and problem-solving.

This 5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation. Finally, you’ll discover how agents move beyond LLM prototypes to become production-ready systems.

5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation. Finally, you’ll discover how agents move beyond LLM prototypes to become production-ready systems.

This 5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation. Finally, you’ll discover how agents move beyond LLM prototypes to become production-ready systems.

This 5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation. Finally, you’ll discover how agents move beyond LLM prototypes to become production-ready systems.

This 5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation. Finally, you’ll discover how agents move beyond LLM prototypes to become production-ready systems.

This 5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation. Finally, you’ll discover how agents move beyond LLM prototypes to become production-ready systems.

This 5-day online course was crafted by Google’s ML researchers and engineers to help developers explore the foundations and practical applications of AI agents. You’ll learn the core components – models, tools, orchestration, memory and evaluation. Finally, you’ll discover how agents move beyond LLM prototypes to become production-ready systems.