{"id":12801,"date":"2026-08-10T10:50:28","date_gmt":"2026-08-10T10:50:28","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-zero-to-generativeai-application-development-mastery-in-2025-2025-5\/"},"modified":"2026-08-10T10:50:28","modified_gmt":"2026-08-10T10:50:28","slug":"udemy-zero-to-generativeai-application-development-mastery-in-2025-2025-5","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-zero-to-generativeai-application-development-mastery-in-2025-2025-5\/","title":{"rendered":"Udemy \u2013 Zero to GenerativeAI Application Development Mastery in 2025 2025-5"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Zero to GenerativeAI Application Development Mastery in 2025. This course is a comprehensive learning path that starts with the fundamentals of AI and machine learning and progresses to building full-fledged generative AI applications. Designed for beginners and those looking to improve their skills, the course covers all the key steps in developing real-world applications. Topics start with the basics of AI, machine learning, deep learning, and their relationship to large language models, and participants are introduced to types of learning including supervised, unsupervised, and reinforcement. It then covers prompt engineering, the components of a structured prompt, the difference between system and user prompts, and how to manage AI illusions. It then goes on to explore core concepts of generative AI and large language models, such as how models like ChatGPT, Claude, and Gemini work, and explains concepts like tokens, context windows, embeddings, fragments, and model parameters in depth. The practical part of the course involves setting up a development environment by installing Python and integrating it with generative AI tools such as ChatGPT, DeepSeek, and Grok, which provide hands-on experience working with models through live coding. Participants learn how to build their own custom applications using the OpenAI, Google Gemini, and DeepSeek APIs, and connect to the models via SDKs, query, and retrieve responses. Hosting options and pricing models for deploying models in the cloud are explored, and cloud-hosted models are used for applications such as text-to-speech, OCR, and computer vision. They also learn how to run models locally on a PC and call them from code without relying on the cloud. Finally, the course concludes with real-world full-stack development projects in which multiple large language models are integrated to solve practical problems. This course is not only a training, but also a gateway to becoming a skilled and confident developer in the field of productive artificial intelligence.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n