Complete Detailed Roadmap To Learn AI In 2025 With Free Videos And Resources

Summary

In this comprehensive video, Krishna Nayak presents a detailed roadmap to learning AI in 2025, tailored for beginners, experienced professionals, and non-technical learners. The guide outlines three main paths: Traditional (strong foundational knowledge in Data Science, ML, CV, NLP), Modern (fast-track mastery of Generative AI and Agentic AI), and Advanced (parallel learning across all areas). The video highlights practical, project-based learning, cloud deployment, and end-to-end application development, supplemented with free YouTube resources, Udemy courses, live classes, and mentorship. Viewers are encouraged to choose their path, track progress, and leverage AI tools for efficient learning and real-world skill acquisition.

Takeaways

• 😀 Generative AI learning involves mastering Python, ML, NLP, and deep learning techniques, with a focus on vector databases and deployment.
• 😀 There is a strong emphasis on understanding vector stores and databases for efficient AI solutions in the generative AI field.
• 😀 To deploy AI projects, platforms like AWS, Azure, Hugging Face, and LangChain are highly recommended for production-level solutions.
• 😀 Agentic AI, a new frontier in AI development, focuses on creating intelligent agents that go beyond generative models and require additional frameworks like LangGraph and Agno.
• 😀 A structured roadmap is provided, offering free resources such as tutorials, GitHub repositories, and detailed week-by-week project guides.
• 😀 The roadmap is divided into three main sections: foundational generative AI, advanced agentic AI, and a section for deploying AI applications.
• 😀 Udemy courses offer more structured, in-depth learning compared to free resources, with regular discount coupons to access these courses affordably.
• 😀 Additional mentorship and live classes are available for personalized support, tailored to both technical and non-technical audiences.
• 😀 The learning approach is inclusive, aiming to cater to all backgrounds, whether you’re from coding or non-coding domains.
• 😀 The goal is to enable career transitions into AI and data science roles, with a focus on hands-on, project-based learning across all stages.

Q & A

Who is the target audience for Krishna Nayak's AI roadmap video?

The target audience includes freshers/college graduates, early-career professionals (0–5 years), experienced professionals (10+ years, including leadership roles), and non-technical professionals from domains like HR and finance.

What are the three learning routes outlined in the AI roadmap?

The three routes are: Traditional Route (start with Data Science), Modern Route (start with Generative AI), and Advanced Route (parallel learning of Data Science, Generative AI, and Agentic AI for technically proficient learners).

What is the recommended sequence for the Traditional Route?

For the Traditional Route, the recommended sequence is Data Science → Machine Learning → Computer Vision → NLP → Generative AI → Agentic AI, with a focus on building end-to-end projects and deploying them using MLOps tools.

What does the Modern Route emphasize for learners?

The Modern Route emphasizes mastering Generative AI first, followed by Agentic AI, and then reinforcing Data Science fundamentals, prioritizing practical application development and industry-relevant skills.

How long does it take to complete the Traditional Route and Modern Route if learning 2–3 hours daily?

The Traditional Route takes approximately 8 months (4 months for Data Science, 2 months for Generative AI, and 2 months for Agentic AI). The Modern Route takes about 6–8 months depending on prior experience.

What are the key modules in Data Science & Classical AI according to the roadmap?

The key modules include Python programming, statistics, exploratory data analysis (EDA), feature engineering, databases (MongoDB, MySQL), machine learning, deep learning, NLP, and MLOps & deployment using tools like AWS, Azure, Docker, DVC, BentoML, Airflow, Grafana, and Evidently AI.

What are the essential topics covered in the Generative AI module?

Generative AI covers Large Language Models (LLMs), diffusion models, prompt engineering, fine-tuning techniques, multimodal AI systems, RAG applications, vector databases, and deployment using frameworks like LangChain, LangGraph, Crewi, Autogen, as well as cloud platforms like AWS, Azure, and Hugging Face.

What is Agentic AI, and what topics are included in this module?

Agentic AI focuses on developing agentic applications and autonomous systems. Topics include building end-to-end projects, frameworks for multi-agent and multimodal applications, RAG applications, and understanding the Model Context Protocol (MCP).

What resources does Krishna Nayak provide to learners?

Krishna provides free YouTube playlists, live session recordings, and structured repositories with week-by-week plans and project checklists. Additionally, he offers affordable Udemy courses and live mentorship programs for more structured guidance.

What strategies are recommended for efficient learning in this roadmap?

Strategies include building real-world projects, using AI tools like ChatGPT, Google Gemini, and Grok to accelerate coding, following the weekly plans in repositories, tracking progress with checklists, and adapting the learning path based on personal experience and career goals.

How can non-technical professionals benefit from this roadmap?

Non-technical professionals can focus on understanding AI applications, workflows, and leadership strategies without deep coding. Krishna offers specific courses tailored for leaders and professionals from non-coding backgrounds to help them transition into AI-driven roles.

Why does Krishna suggest starting with Generative AI in the Modern Route?

Starting with Generative AI allows learners to quickly gain industry-relevant skills in building LLM-based applications, which are in high demand. It also enables learners to focus on practical application development first, while gradually acquiring foundational knowledge in Data Science and ML.