This will be a series of notebooks and work to teach the python in the year 2026 for the young coder who wants to enter in the field of AI, Data Science, Gen AI.
🚀 Python for the AI Future: The 2026 Young Coder Roadmap Welcome to the definitive learning repository designed for aspiring young coders eager to launch a career in Artificial Intelligence (AI), Data Science, and Generative AI (Gen AI) by 2026. This is not just another Python tutorial; it's a curated, project-based roadmap built on Jupyter Notebooks and modern best practices to ensure you gain skills immediately relevant to the next wave of tech innovation.
🎯 Why This Repo? The landscape of AI is rapidly evolving, with Gen AI becoming a core skill. This roadmap is structured to bypass outdated concepts and focus squarely on the Python "AI Dialect": the essential libraries, frameworks, and practical projects that industry professionals actually use. We begin with a strong foundation in Python and immediately pivot to applied data science and AI concepts.
🗺️ The Learning Path (Notebooks & Workbooks) Our curriculum is divided into phases, with each folder containing hands-on notebooks:
Python Core & Data Foundation: Master Python fundamentals, object-oriented programming (OOP), and the essential data stack: NumPy for efficient computation and Pandas for data manipulation and cleaning. This phase emphasizes using the Jupyter Notebook environment—the standard tool for data scientists.
Machine Learning & Deep Learning Foundations: Dive into the core of AI with Scikit-learn for classic ML algorithms (e.g., classification, regression) and introductory TensorFlow/PyTorch for neural networks. We focus on critical concepts like model evaluation and feature engineering.
Generative AI & Agentic Systems (The 2026 Focus): This is where you future-proof your skills. Learn Prompt Engineering, how to interact with Large Language Models (LLMs) via the OpenAI/Hugging Face APIs, and build your first intelligent applications using Agentic Frameworks like LangChain and LlamaIndex for Retrieval-Augmented Generation (RAG).
Projects and Deployment: Move from theory to application by building portfolio-worthy projects like a Smart Chatbot, a Data Visualization Dashboard, or an Image Classifier. We also cover basic Git/GitHub workflow and introduce Docker for sharing and deploying your models efficiently.
👥 For Who? This repository is perfect for complete beginners who want a fast-track into an AI-focused career, or for students with basic coding knowledge looking to specialize in the cutting-edge fields of Data Science and Generative AI. All notebooks are designed for clear, step-by-step learning.