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AI Courses at LinkedIn Learning
Are you interested in learning more about GenAI, and how it can support your work? Check out the LinkedIn Learning Upskilling Framework that will help to guide your learning choices. Here is a summary of the opportunities:
| Level & Featured Skills | Learning Type | Who will benefit? |
| Level One: AI literacy, generative AI, responsible AI | Understanding | For all professionals, including leaders/managers. Includes responsible AI, GenAI fluency, and awareness. |
| Level Two: Prompt engineering, strategy, copilots and AI pair programming, AI productivity | Applying | For all professionals, including leaders/managers. Includes prompt engineering and copilots. |
| Level Three: No/low code GAI, GPTs, APIs, databases | Building | For power users and developers building AI-powered applications and solutions. Includes low-code methodologies and hands-on practice with APIs. |
| Level Four: Building ML models, deep learning, neural networks, NLP, AI tools and libraries | Training and Maintaining Models | For engineers implementing AI/Machine Learning. Includes technical applications of AI and ML models, deep learning, and fine-tuning models. |
| Level Five: AIOps, MLOps, LLMOps, AI security, AI cloud solutions | Deeply Specializing | For tech specialists and R&D roles. Includes DevOps, ML researchers, cybersecurity, and data scientists. |
AI Upskilling Framework: Abbreviations Glossary
| Abbreviation / Term | Definition |
| AI | Artificial Intelligence — machines or systems that simulate human intelligence (learning, reasoning, decision-making). |
| GenAI | Generative Artificial Intelligence — a subset of AI that can generate new content (text, images, audio) based on patterns learned from data. |
| ML | Machine Learning — a subset of AI where systems improve at tasks over time by learning from data rather than being explicitly programmed. |
| DL | Deep Learning — a branch of ML using neural networks with many (“deep”) layers to model complex patterns in data. |
| NLP | Natural Language Processing — the field of AI that focuses on the interaction between computers and human (natural) language: understanding, interpreting, generating. |
| NLG | Natural Language Generation — a sub-area of NLP concerned specifically with generating coherent, human-like text from structured or unstructured data. |
| LLM | Large Language Model — a language model (often transformer-based) trained on massive text corpora, used for tasks like generation, summarization, translation. |
| GPT | Generative Pre-trained Transformer — a type of LLM developed by OpenAI; “pre-trained” on large text data, then fine-tuned for specific tasks. |
| FM | Foundation Model — large, versatile models (like LLMs) trained on broad data, which can be adapted (fine-tuned) for many downstream tasks. |
| MLOps | Machine Learning Operations — practices and tools to manage the deployment, monitoring, governance, and lifecycle of ML models in production. |
| LLMOps | Large Language Model Operations — specialized operations practices for managing LLMs (training, serving, fine-tuning, monitoring) in production. |
| LoRA | Low-Rank Adaptation — a parameter-efficient fine-tuning technique that adapts large models by modifying a small number of additional parameters. |
| PEFT | Parameter-Efficient Fine-Tuning — methods such as Low-Rank Adaptation (LoRA) that adapt large models with minimal parameter updates, making fine-tuning more efficient. |
| RAG | Retrieval-Augmented Generation — technique where an LLM retrieves external knowledge (documents, databases) to produce more accurate or up-to-date outputs. |
| RLHF | Reinforcement Learning from Human Feedback — a training method where humans provide feedback to model outputs, and the model is fine-tuned to align more with human preferences. |
| Prompt Engineering | The practice of designing and structuring inputs (“prompts”) to guide AI models toward better, more accurate responses. |
| XAI | Explainable AI — methods and approaches that make AI system decisions transparent, interpretable, and understandable to humans. |
| CI/CD | Continuous Integration / Continuous Deployment — software development practices that help teams integrate code frequently and deploy updates reliably, often used in MLOps. |
| API | Application Programming Interface — a set of protocols for building and interacting with software applications; used by developers to call AI services. |
| Copilot | AI assistant tools (e.g., GitHub Copilot, Microsoft Copilot) that help users by generating content, code, or tasks, often via prompts. |
| DevOps | Development + Operations — culture and set of practices that unify software development and IT operations; relevant in AI engineering for deploying and maintaining systems. |
| AI Governance | Frameworks, policies, and processes to ensure AI is used responsibly, ethically, and in compliance with regulation. |