Image: Tanvir Khan

Generative AI is fundamentally changing how we work and is set to explode the market, with forecasts expecting it to reach over $638 billion by 2028. Businesses are heavily investing in AI solutions to boost efficiency and drive growth. This makes having AI skills less of a nice-to-have and more of a necessity.

Existing roles like software development, product management, and data science now require a certain level of comfort and capability with AI technologies. Think being able to integrate large language models (LLMs) into software applications, or combining data analysis techniques with generative AI for deeper insights. On top of that, we're seeing a rise in entirely new specialized roles, like LLM Engineers, AI Engineers, and AI Architects.

Whether your goal is to stay current in your field to earn a higher salary, or you're aiming to land a job in the tech sector, gaining practical AI skills is essential today.

With that in mind, I've hand-picked three excellent courses designed to equip you with the generative AI skills needed to stay competitive and thrive in today's tech industry.

Recommended Courses for Practical AI Skills

Let's dive into the details of each course:

1. Microsoft's "Generative AI for Beginners"

Microsoft Generative AI for Beginners Course on GitHub

Microsoft offers this course as a truly comprehensive resource for generative AI, and best of all, it's completely free! Structured as a GitHub repository, this program includes over 20 lessons covering both the core concepts (theory) and hands-on application (practical) of generative AI.

Here are some key areas you'll cover in this learning path:

Generative AI Fundamentals: The course starts with the basics, explaining what generative AI is and how to choose the right type of model for different tasks you want to accomplish.

Mastering Prompt Engineering: You'll learn effective techniques to write prompts that guide AI models to produce the results you want. This includes understanding approaches like zero-shot (asking the AI to do something without examples) and few-shot prompting (providing a few examples), plus methods to make the AI's output more predictable.

Building Text and Image Generation: Moving beyond theory, this course teaches you how to build actual text and image generation tools. You'll implement more complex concepts like semantic search, which is already used in products from companies like Notion and Spotify. Learning these practical implementations is highly valuable for job prospects in the AI era.

Implementing RAG Systems: Retrieval Augmented Generation (RAG) is a powerful technique. It combines the broad knowledge of LLMs with specific, reliable information from your own knowledge base (like internal company documents) to make the LLM better at certain tasks. This is a major use case for LLMs right now; for example, data scientists in my own company are working on building robust RAG systems using our internal data. This course will teach you how to build RAG systems using techniques like vector databases and embeddings – valuable skills that employers are looking for.

Introduction to AI Agents: AI agents have generated a lot of excitement lately, often discussed as the future of work for their ability to automate tasks. Put simply, an AI agent is a system designed to plan and carry out complex workflows on its own, without constant instructions from a human. Microsoft's course will cover:

• What AI agents are and their different types.

• How to build an application featuring an AI agent, using a project example of pitching a new product idea.

Fine-Tuning Large Language Models: Fine-tuning involves customizing a powerful pre-trained language model by training it further on your own specific dataset. This process tailors the foundational model to your particular use case, significantly boosting its performance for that specific task. The Microsoft course will guide you through fine-tuning OpenAI's popular GPT-3.5 models for a specialized area, and teach you how to deploy and use the refined model.

2. Hugging Face Reasoning Course

Hugging Face Reasoning Course Webpage

Part of the larger Hugging Face LLM course series, the Reasoning Course focuses specifically on building AI models capable of better reasoning. It's structured in a cohort style, meaning lessons are released step-by-step over time. If you prefer a structured learning schedule and enjoy learning alongside others, this format is great.

This course is particularly recommended if you already have some foundational knowledge of LLMs and want to delve into making these models think and reason more effectively.

Here's what you can expect to learn:

Reasoning Fundamentals: Get an introduction to concepts from reinforcement learning and understand how these principles can be applied to improve the reasoning abilities of language models.

Understanding the DeepSeek R1 Research: You'll explore the research behind models like DeepSeek R1, learning how they can "learn" through experimentation and trial-and-error using reinforcement learning methods.

Advanced GRPO Explained: You'll learn about GRPO, a specific reinforcement learning technique used in models like DeepSeek R1. Discover how GRPO can reduce the amount of computing power needed compared to techniques used in models like ChatGPT, enabling fine-tuning with less powerful hardware.

Applying GRPO with Unsloth: Gain practical skills in fine-tuning models using GRPO with the help of the Unsloth software library.

Beyond these core topics, the course includes interactive code reviews and live sessions where you can learn how to build "Open R1," which is an open-source project aiming to replicate the DeepSeek R1 model. (Note: This is an ongoing course with new material released weekly; access includes previous lessons).

3. Hugging Face Agents Course

Hugging Face AI Agents Course

The Hugging Face Agents Course teaches you the practical skills to build and deploy functional AI agents. As mentioned earlier, AI agents are systems capable of taking actions autonomously to achieve specific goals without continuous human instruction.

Here are the main subjects covered in this course:

Introduction to AI Agents: Learn the definition of AI agents, get a refresher on how LLMs operate, and build your very first AI agent during this initial lesson.

Exploring Agent Frameworks: This lesson introduces you to three widely-used software frameworks designed to simplify the process of building and managing complex AI agent workflows.

Using Agentic RAG: We touched on RAG earlier as a way for LLMs to retrieve relevant data from your own sources. This lesson focuses on "agentic RAGs," where agents are used to interact with your data sources to answer complex questions.

Final Project: The course culminates in a final challenge where you'll apply all the concepts learned to create a working AI agent. The goal is to build an agent that performs well on the GAIA benchmark, a standard evaluation for AI agents.

This course covers both the underlying principles of AI agent design and the practical steps to build them, with a significant final project to solidify your skills.

Regarding certification:

• Completing the first unit qualifies you for a "fundamentals" certificate.

• To earn a "certificate of completion," you must finish Unit 1, one assigned task, and the final challenge project.

Be aware that you need to complete these assignments before July 1, 2025, to qualify for certificates. However, if you find the course after this deadline, all the learning materials will still be available to you.

Overview

To recap, I've highlighted three practical generative AI courses available in 2025, suitable for different skill levels:

• Microsoft’s Generative AI for Beginners: Great for starting from scratch with a broad overview and hands-on coding.

• Hugging Face Reasoning Course: Ideal for those with some LLM background who want to improve model intelligence and reasoning capabilities.

• Hugging Face Agents Course: Focuses specifically on the exciting field of building autonomous AI agents.

If your time is limited and you can only take one course, the Microsoft "Generative AI for Beginners" program is an excellent starting point as it covers a wide range of fundamental and practical topics. Once you've completed that and want to specialize in areas like building AI agents or developing advanced reasoning models, the other two Hugging Face courses are perfect next steps.

Conclusion

The landscape of technology careers is rapidly evolving, with practical generative AI skills quickly becoming essential. Investing time in these courses can significantly boost your employability and career prospects. Don't wait – pick a course that matches your current level and start building the skills that will be in demand for years to come!