Generative AI for Beginners 2025:

Microsoft has released a free and comprehensive course titled “Generative AI for Beginners,” which teaches the concepts of generative artificial intelligence from beginner to advanced levels in 18 episodes. This course is designed for AI enthusiasts, developers, students, and professionals in the technology field, and includes instructional videos, coding examples in Python and TypeScript, practical exercises, and supplementary resources.

• Access Mode: Online
• Proivder: Microsoft
• Degree: No-Degree is Needed
• Subject: Learning AI with Microsoft
• Funded: Free
• Deadline: Not Mentioned

Part 1: Introduction to Generative Artificial Intelligence and Large Language Models (LLMs):

• Familiarization with the basic concepts of generative artificial intelligence, how large language models work, and their applications in various industries.

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Part 2: Examination and Comparison of Different Language Models:

• Comparison of various large language models, their applications, and how to choose the appropriate model for different needs.

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Part 3: Responsible Use of Generative Artificial Intelligence:

• Exploration of ethical principles and accountability in the use of generative artificial intelligence and strategies to prevent misuse.

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Part 4: Understanding the Principles of Prompt Engineering:

• Familiarization with the concepts of prompt engineering, its components, and techniques for optimizing commands to receive better responses from AI models.

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Part 5: Creating Advanced Prompts:

• Learning advanced techniques in prompt engineering to improve the quality and accuracy of responses from language models.

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Part 6: Building Text Generation Applications:

• Training on how to create text generation applications using the OpenAI library and related concepts such as prompts, temperature, and tokens.

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Part 7: Building Chat Applications:

• Guidance on creating and integrating AI-based chat applications, along with important tips for their design and implementation.

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Part 8: Building Vector Database Search Apps:

• Introduction to the concept of Embeddings, creating a vector database, and implementing semantic search in applications.

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Part 9: Building Image Generation Applications:

• Training on the use of image generation models such as DALL-E and Midjourney, and creating image generation applications.

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Part 10: Building AI Applications with Low Code:

• An overview of low-code development platforms and the use of artificial intelligence in them.

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Part 11: Integrating External Applications with Function Calls:

• Familiarization with the concept of function calls, their applications, and how to integrate them into applications using Azure OpenAI.

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Part 12: UX Design for AI Applications:

• An examination of the principles of user experience (UX) design in AI applications and creating trust and transparency in them.

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Part 13: Securing Artificial Intelligence Applications:

• Identifying threats and risks associated with artificial intelligence systems and methods for securing them.

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Part 14: Lifecycle of Generative Artificial Intelligence Applications:

• Examining the lifecycle of generative AI applications and how to maintain and update them to keep pace with technological advancements.

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Part 15: Retrieval-Augmented Generation (RAG) and Database Querying:

• Familiarization with the concept of RAG, its applications, and how to integrate it with querying databases in applications.

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Part 16: Open Source Models and Hugging Face

• An overview of open-source language models, their advantages, and the use of platforms like Hugging Face and Azure AI Studio.

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Part 17: Artificial Intelligence Agents

• Familiarization with the concept of artificial intelligence agents, their various frameworks, and diverse applications in AI programs.

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Part 18: Fine-tuning Large Language Models

• Learning the concept of fine-tuning language models, the benefits, challenges, and how to implement them to improve model performance.

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