Best AI Courses, Roadmaps & Reports for Beginners in 2025

Summary

This video emphasizes the importance of transitioning from being an AI user to an AI builder in order to stay competitive in the evolving job market. It highlights a range of free and paid resources for learning AI, including courses on prompt engineering, building systems with APIs, and using AI for design and prototyping. The video stresses the value of consistent learning and the risk of becoming unemployable due to a lack of AI skills. Viewers are encouraged to start learning today, leveraging accessible tools and resources to build AI expertise.

Takeaways

• 🚀 Becoming an AI builder provides a comparative advantage over just being an AI user, increasing employability in emerging AI-driven industries.
• 📌 The real career risk is being unemployable, not AI-driven unemployment; learning AI skills now helps secure future opportunities.
• 💻 Free resources like DeepLearning.AI, Microsoft Copilot Academy, OpenAI Academy, Google GenAI, NVIDIA, Hugging Face, and Anthropic offer foundational learning in AI, LLMs, RAG models, and prompt engineering.
• 🎓 Paid professional certifications (Simply Learn + IIT collaborations) provide in-depth knowledge, hands-on projects, live mentorship, and exposure to industry tools.
• 🧠 Python is essential for programming AI, but focus should be on logical thinking and systems design rather than just coding syntax.
• 📊 AI reports like AI in 2027, AI Engineering Report, State of AI in Design, and SLM reports provide strategic insights on AI trends, adoption, and future models.
• 📝 Effective learning requires setting clear deadlines, documenting notes in tools like Notion, and organizing insights using methods like NotebookLM.
• 🌐 Learning in public—sharing progress on LinkedIn, X (Twitter), and Instagram—reinforces understanding and builds visibility in the AI community.
• ⚙️ RAG and SLM models highlight the importance of augmenting intelligence with custom context layers and efficient small language models for cost-effective AI applications.
• 💡 Continuous learning and active engagement with AI tools prevent intellectual stagnation and help professionals leverage AI as a catalyst rather than a threat.
• 🎨 Strong UX and design skills combined with AI knowledge make individuals highly employable, as great design makes intelligence accessible to a wider audience.

Q & A

What is the key difference between an AI builder and an AI user, as discussed in the video?

An AI user operates tools and systems created by others, while an AI builder creates those tools and systems. The advantage of being an AI builder is long-term employability, as the demand for creators of AI solutions will increase.

What does the speaker suggest as the first step in becoming an AI builder?

The first step is to focus on learning the basics of AI, including prompt engineering, APIs, and building small, functional systems. The speaker emphasizes starting small and setting clear deadlines to achieve consistent progress.

How does the speaker recommend structuring AI learning resources?

The speaker advises starting with free resources and then moving to paid certifications. Key platforms include DeepLearning.ai, Microsoft Copilot Studio Academy, OpenAI Academy, and others. The idea is to build a solid foundation with free content before committing to paid courses or professional certifications.

Why is documenting and sharing your AI learning important?

Documenting learning in platforms like Notion and sharing it publicly (e.g., LinkedIn, Twitter) creates accountability, enhances learning, and helps build a professional brand. The speaker suggests using the waterfall model for content creation to document learning and share insights progressively.

What is the 'waterfall model' for content creation, and why is it useful?

The waterfall model involves documenting insights as you learn and then sharing them progressively. It's useful because it ensures a consistent flow of learning, keeps content organized, and helps others benefit from your progress while also holding you accountable.

How can Notebook LM be used effectively according to the speaker?

Notebook LM is recommended as a tool for analyzing AI reports, extracting key insights, and making sense of complex information. It's particularly useful for students to digest reports and make them actionable in their AI learning journey.

What is the significance of SLMs in the context of future AI development?

SLMs (Small Language Models) are seen as the future of agentic AI due to their lower latency, lower computational and memory requirements, and cost-efficiency. The speaker highlights that SLMs can be 10 times cheaper than large language models (LLMs), making them a more sustainable option for widespread AI deployment.

Why does the speaker emphasize the importance of professional development in AI?

The speaker emphasizes professional development because AI tools can quickly replace non-professionals, but continuous learning and expertise in systems thinking, logical problem solving, and human-in-loop models can help professionals stay relevant and competitive in the AI space.

What potential risk does AI pose to the workforce, and how can individuals mitigate it?

AI could lead to 'intellectual obesity,' where individuals rely too much on AI without developing their own cognitive skills. To mitigate this risk, the speaker recommends staying engaged with learning, using AI tools as augmentations, and focusing on high-value areas like logical thinking and systems building.

What is the importance of understanding trends like SLMs and RAG in the context of AI careers?

Understanding trends like SLMs (Small Language Models) and RAG (Retrieval-Augmented Generation) is critical because they represent the future of cost-effective and scalable AI systems. Professionals who understand these trends will be better equipped to develop innovative AI solutions and remain competitive in the job market.