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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →For a quick introduction to large language models (LLMs), start with Google Skills’ one-hour course. For a broader grounding in AI, choose Microsoft Learn’s seven-module path. If you want to work directly with LLMs in code, Hugging Face’s course is the strongest fit—and the only option here whose provider explicitly says it is completely free and ad-free. The other courses may have access conditions, so check their current terms before starting.
Compare the five courses
| Course | Best for | Scope and format | Starting knowledge | Free-access wording |
|---|---|---|---|---|
| Microsoft Learn: Introduction to generative AI and agents | A focused primer on LLMs, prompts and agents | Beginner module; listed objectives cover how LLMs work, effective prompts and agent concepts | Familiarity with AI concepts and terminology, plus a basic understanding of machine learning | Price or permanent free access is not stated on the captured page. |
| Google Skills: Introduction to Large Language Models | A short LLM orientation | Introductory, one-hour course on LLMs, use cases, prompt tuning and Google tools for generative-AI apps | Not stated on the captured page | Price or permanent free access is not stated in the captured course description; check platform access conditions. |
| Microsoft Learn: AI concepts for developers and technology professionals | Broad AI foundations | Beginner path with seven modules spanning generative AI and agents, computer vision, speech, NLP, information extraction and RAG | Basic computing and math familiarity recommended | Price or permanent free access is not stated on the captured page. |
| Hugging Face LLM Course | Hands-on LLM and NLP learning | Practical curriculum using Transformers, Datasets, Tokenizers, Accelerate and the Hugging Face Hub | Good Python knowledge; introductory deep learning first is recommended | Hugging Face says the course is “completely free and without ads.” |
| Microsoft Learn: Introduction to Microsoft AI models | Learning Microsoft’s model ecosystem | Beginner path with three modules on reasoning, code generation, speech, transcription and image generation, including models in Microsoft Foundry | Generative-AI familiarity, Microsoft Foundry experience and familiarity with AI-assisted coding concepts | Price or permanent free access is not stated on the captured page. |
“Free” is not equally clear across these listings: Hugging Face makes an explicit free-and-ad-free claim, while the captured descriptions for Google Skills and Microsoft Learn do not establish permanent free access. Check the course page for current access terms, especially if you need to know whether an account or other platform condition applies.
1. Hugging Face LLM Course: best for learning by building
Hugging Face’s LLM Course is the most substantial coding-oriented choice in this group. Its early chapters introduce Transformer models and how to use them; later material moves into fine-tuning, sharing models, datasets and tokenizers, classical NLP, demos, dataset curation, fine-tuning LLMs and building reasoning models. The course uses Hugging Face’s Transformers, Datasets, Tokenizers and Accelerate libraries, along with its Hub.
This is not an ideal first stop if you are new to programming or deep learning. Hugging Face expects good Python knowledge and recommends introductory deep-learning experience. Its suggested pace is 6–8 hours per chapter-week, and its FAQ says there is currently no certification. The course is released under Apache 2, according to its course page.
#1 Best Overall
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2. Google Skills: Introduction to Large Language Models: best for a quick orientation
Google Skills’ Introduction to Large Language Models is an introductory course listed at one hour, making it the shortest clearly timed option in this guide. Its description covers what LLMs are, use cases, prompt tuning and Google tools for building generative-AI applications. It is a sensible starting point if you want an overview before choosing a deeper or more technical course.
The captured course description does not promise permanent free access. Confirm the current access conditions on Google Skills before enrolling.
Rank #2
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3. Microsoft Learn: AI concepts: best for broad AI foundations
Microsoft Learn’s AI concepts path is broader than a dedicated LLM course. Its seven beginner modules range across generative AI and agents, computer vision, speech, natural language processing, information extraction and retrieval-augmented generation (RAG). Choose it if you want to understand how LLMs sit alongside other common AI workloads, rather than concentrating only on text-generation models.
Microsoft recommends basic familiarity with computing and math. The captured page does not state a price or guarantee permanent free access.
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4. Microsoft Learn: Introduction to generative AI and agents: best focused primer
This Microsoft Learn module focuses on generative AI, LLMs, prompts and agents. Its listed learning objectives include explaining how LLMs work, creating effective prompts and describing agent concepts. It suits learners who want a compact, focused primer and already know basic AI terminology and machine-learning concepts.
The page lists those topics as prerequisites; it does not state a price or permanent free-access terms in the captured text.
Rank #4
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5. Microsoft Learn: Introduction to Microsoft AI models: best for Microsoft-tool users
Microsoft’s AI models path covers models for reasoning, code generation, speech, transcription and image generation, including their use in Microsoft Foundry. Its three modules are aimed at learners who already have some generative-AI knowledge and experience with Microsoft Foundry, plus familiarity with AI-assisted coding concepts. Those prerequisites make it a poor first course for a complete beginner, but a practical platform-specific option for someone already working in that ecosystem.
The captured page does not state a price or guarantee permanent free access.
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Choose a course or learning sequence
- For a quick overview: Start with Google Skills’ one-hour LLM introduction.
- For broader AI literacy: Take Microsoft Learn’s seven-module AI concepts path.
- For prompts and agents: Choose Microsoft Learn’s focused generative-AI module if its prerequisites match your background.
- For hands-on LLM skills: Work through Hugging Face after you have good Python skills and introductory deep-learning knowledge.
- For Microsoft Foundry work: Use the Microsoft AI models path once you meet its platform and AI-assisted-coding prerequisites.
A reasonable progression is a short orientation with Google Skills or Microsoft’s generative-AI module, followed by the broader AI concepts path, then Hugging Face for deeper coding practice. This is a suggested sequence based on the published course descriptions, not a measured claim about learning outcomes.
An advanced option: DeepLearning.AI Pretraining LLMs
DeepLearning.AI’s Pretraining LLMs is an intermediate course listed at 1 hour 19 minutes, with eight video lessons and six code examples. Its listing describes data preparation, packaging training data, model initialization, training and evaluation. The listing says access is free for a limited time during the learning platform beta, so verify availability and terms before relying on it as a free option.
Optional further reading
For traditional NLP topics such as naive Bayes and LSTMs, the Hugging Face course recommends Natural Language Processing with Transformers. The course page identifies contributors who co-wrote the book; reading it is optional, not a prerequisite.
Quick Recap
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