Right Way To Learn AI In 2025

Summary

In this video, Krishna shares insights on how to efficiently learn AI, whether you're a beginner or an experienced professional. He emphasizes starting with Python, moving through machine learning and deep learning, and then exploring generative AI, agentic AI, and LLMs. For freshers, he recommends a step-by-step approach, while for professionals, he suggests diving directly into generative AI and learning foundational concepts in parallel. He also advises non-coders like managers to use no-code platforms. Throughout, Krishna highlights the importance of staying updated with AI trends and offers resources for further learning.

Takeaways

• 😀 AI technology has rapidly evolved, particularly in machine learning, deep learning, NLP, computer vision, LLMs, and generative AI.
• 😀 Beginners should start learning AI by mastering a programming language, with Python being the top recommendation for AI and data science.
• 😀 After learning Python, the next step is to dive into machine learning, covering important concepts such as supervised and unsupervised learning.
• 😀 Deep learning comes after machine learning, focusing on specialized areas like NLP and computer vision, with NLP becoming increasingly popular due to advancements like transformers.
• 😀 Generative AI is a game-changer, enabling the development of applications like chatbots, content generation, and summarization through LLM models.
• 😀 Generative AI frameworks like Langchain and OpenAI have made it easier to develop AI applications without the need to train models from scratch.
• 😀 AI agents and agentic AI applications are gaining traction, offering solutions to automate tasks like managing cloud resources or building chatbots for specific data.
• 😀 For beginners, a structured path from programming language to machine learning, deep learning, generative AI, and agentic AI is crucial for thorough learning.
• 😀 Experienced professionals transitioning to AI can jump directly into generative AI, but should also learn foundational concepts like deep learning and machine learning in parallel for deeper understanding.
• 😀 Non-technical professionals, such as program managers, can leverage no-code platforms like Nin and Langflow to create generative AI applications and understand AI concepts without coding.
• 😀 Continuous learning is essential as AI evolves rapidly, with new frameworks, tools, and use cases emerging regularly in fields like generative AI and agentic AI.

Q & A

What is the recommended starting point for freshers learning AI?

Freshers should start by learning Python, as it is the primary language for AI development. From there, they should dive into machine learning basics, including supervised and unsupervised learning, before moving on to deep learning and agentic AI.

Why is Python considered the best programming language for AI?

Python is considered the best programming language for AI because of its simplicity, extensive libraries, and community support, which make it ideal for machine learning, deep learning, and AI development.

What are the essential AI concepts that freshers need to learn after Python?

After learning Python, freshers should focus on machine learning (supervised and unsupervised learning), deep learning (especially NLP and computer vision), and then move towards generative AI, including Large Language Models (LLMs).

What is agentic AI, and why is it important for the future of AI?

Agentic AI refers to intelligent systems or agents that autonomously take actions based on their learning. It is important because it enables the development of AI systems that can perform complex tasks autonomously, such as interacting with other AI or solving real-world problems without direct human intervention.

How can experienced professionals transition into AI, especially if they come from non-technical backgrounds?

Experienced professionals, especially from non-technical backgrounds, can start by learning generative AI tools. They should then focus on understanding the foundational concepts of machine learning and deep learning to gain a complete understanding of AI technology.

What learning resources are recommended for individuals looking to stay updated on AI advancements?

Udemy courses, YouTube tutorials, and free resources provided by experts in the field are great ways to stay updated. Additionally, continuously following the latest developments and new frameworks like LangChain in generative AI is crucial.

What role do no-code tools play in AI development, especially for non-technical professionals?

No-code tools allow non-technical professionals, especially program managers, to build AI applications without needing programming knowledge. Tools like Nin and LangFlow enable users to design and deploy AI-driven solutions using a visual interface.

How does the AI learning path differ for freshers compared to experienced professionals?

Freshers should follow a structured path starting with basic programming (Python), followed by machine learning, deep learning, and agentic AI. Experienced professionals, on the other hand, can jump directly into generative AI but must understand the foundational concepts of machine learning and deep learning in parallel.

What are the key challenges faced by program managers who want to get involved with AI?

Program managers without coding knowledge face the challenge of understanding the technical aspects of AI. However, no-code tools help them grasp the concepts and develop AI applications without needing to write code themselves.

What are some of the most popular applications of AI that freshers should focus on learning?

Freshers should focus on areas such as Natural Language Processing (NLP) for text-based applications, computer vision for image-related tasks, and generative AI for creating innovative solutions. Understanding and applying frameworks like LangChain for agentic AI is also essential.