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The complete AI, ML & GenAI roadmap. I've given a stepwise breakdown to master them. 𝗦𝘁𝗲𝗽 1 – 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 (1.5–2 𝗺𝗼𝗻𝘁𝗵𝘀) • Build core skills: Python, math, data handling, Git. • Learn 𝗡𝘂𝗺𝗣𝘆, 𝗣𝗮𝗻𝗱𝗮𝘀, 𝗠𝗮𝘁𝗽𝗹𝗼𝘁𝗹𝗶𝗯, Jupyter/Colab, VS Code. • Study basic algebra, probability, calculus. • Intro to cloud platforms (AWS/GCP/Azure). • This sets the base for all advanced AI topics. 𝗦𝘁𝗲𝗽 2 – 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗕𝗮𝘀𝗶𝗰𝘀 (2 𝗺𝗼𝗻𝘁𝗵𝘀) • Learn supervised & unsupervised algorithms. • Use 𝗦𝗰𝗶𝗸𝗶𝘁-𝗹𝗲𝗮𝗿𝗻, 𝗫𝗚𝗕𝗼𝗼𝘀𝘁 for model building. • Do feature engineering, tuning, evaluation. • Build small ML apps with 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 / 𝗙𝗹𝗮𝘀𝗸. • Convert raw data into usable predictive models. 𝗦𝘁𝗲𝗽 3 – 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 (1.5 𝗺𝗼𝗻𝘁𝗵𝘀) • Learn neural networks: 𝗔𝗡𝗡, 𝗖𝗡𝗡, 𝗥𝗡𝗡, 𝗟𝗦𝗧𝗠. • Work on image tasks like classification & detection. • Train models in 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 / 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄. • Explore 𝗩𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 (𝗩𝗶𝗧) & diffusion models. • Build systems that understand images. 𝗦𝘁𝗲𝗽 4 – 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 & 𝗟𝗟𝗠𝘀 (2 𝗺𝗼𝗻𝘁𝗵𝘀) • Study Transformers, BERT, GPT architecture basics. • Learn prompt engineering & LangChain pipelines. • Fine-tune models with 𝗟𝗼𝗥𝗔 / 𝗤𝗟𝗼𝗥𝗔 / 𝗣𝗘𝗙𝗧. • Build RAG systems for factual responses. • Create real GenAI apps like chatbots & agents. 𝗦𝘁𝗲𝗽 5 – 𝗠𝗟𝗢𝗽𝘀 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 (1.5 𝗺𝗼𝗻𝘁𝗵𝘀) • Use 𝗠𝗟𝗳𝗹𝗼𝘄, 𝗞𝘂𝗯𝗲𝗳𝗹𝗼𝘄, 𝗠𝗲𝘁𝗮𝗳𝗹𝗼𝘄 for workflows. • Deploy models on 𝗦𝗮𝗴𝗲𝗠𝗮𝗸𝗲𝗿, 𝗩𝗲𝗿𝘁𝗲𝘅 𝗔𝗜, 𝗔𝘇𝘂𝗿𝗲 𝗠𝗟. • Learn Docker, FastAPI, A/B testing. • Add monitoring, retraining & versioning. • Make models production-ready and scalable. 𝗦𝘁𝗲𝗽 6 – 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 1. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 • Use 𝗖𝗿𝗲𝘄𝗔𝗜, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 for tool-using agents. • Build multi-step automation workflows. 2. 𝗦𝗽𝗲𝗲𝗰𝗵 & 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 • Work with 𝗦𝗧𝗧/𝗧𝗧𝗦, 𝗥𝗶𝘃𝗮, 𝗘𝗹𝗲𝘃𝗲𝗻𝗟𝗮𝗯𝘀. • Build voice assistants & chat interfaces. 3. 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 & 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • Combine text–image–audio models (CLIP/BLIP). • Use 𝗙𝗔𝗜𝗦𝗦 / 𝗖𝗵𝗿𝗼𝗺𝗮 for semantic search. 4. 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 & 𝗔𝗜 𝗦𝗮𝗳𝗲𝘁𝘆 • Explore 𝗚𝗔𝗡𝘀, 𝗠𝗟-𝗔𝗴𝗲𝗻𝘁𝘀, bias checks & explainability. 𝗙𝗶𝗻𝗮𝗹 𝗙𝗹𝗼𝘄 (𝗛𝗼𝘄 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝗽𝗹𝗮𝗻 𝘄𝗼𝗿𝗸𝘀) 1. Build coding + math fundamentals 2. Learn core ML concepts 3. Move to deep learning & computer vision 4. Master LLMs & generative AI 5. Learn deployment, pipelines & MLOps 6. Choose specializations and build real projects ✅ Repost for others in your network who want to build a career in AI.