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Unclaimed ProfileThis comprehensive program on Generative AI, meticulously crafted by Krish Naik and Jayant Topnani alongside KRISHAI Technologies, serves as an ultimate guide for developers, data scientists, and tech professionals looking to master the modern AI landscape. Throughout this extensive 57.5-hour journey, learners dive deep into the practical application of LangChain and Hugging Face—two of the most dominant frameworks in the natural language processing and large language model domains today. By bridging theory and hands-on implementation, the course enables students to build state-of-the-art Retrieval-Augmented Generation (RAG) pipelines, which significantly improve LLM accuracy and prevent hallucinations by integrating real-time data retrieval mechanisms. Aspiring AI practitioners will explore model fine-tuning, custom prompt engineering, and the seamless integration of pre-trained Hugging Face transformers into modular LangChain workflows. Beyond building models, the course places a strong emphasis on production-ready deployment, guiding students through the complexities of scaling AI systems on cloud platforms and on-premise environments. Whether you aim to design advanced conversational agents, automate content generation, or architect robust enterprise-grade AI solutions, this curriculum provides the exact blueprint needed to transition from an AI enthusiast to a highly competent Generative AI engineer.
About the creator
Krish Naik, Jayant Topnani, KRISHAI Technologies Private Limited
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Krish Naik, alongside co-instructor Jayant Topnani and the educational powerhouse KRISHAI Technologies Private Limited, represents a leading force in modern data science, artificial intelligence, and programming education. With a massive global community of learners, Krish has pioneered accessible tech education, translating complex theoretical frameworks into practical, project-based learning experiences. Their educational philosophy is deeply rooted in hands-on implementation, bridging the gap between academic theory and industry demands. Across their curated bootcamps—ranging from comprehensive Generative AI courses using cutting-edge tools like LangChain and Hugging Face, to foundational Python coupled with rigorous Data Structures and Algorithms (DSA) exercises, and extensive Data Analyst…Show more
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Program Overview
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Learning format
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Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Learn to create advanced generative AI applications leveraging the Langchain framework and Huggingface's state-of-the-art models.
- Understand the architecture and design patterns for building robust generative AI systems.
- Gain hands-on experience in deploying generative AI models to various environments, including cloud platforms and on-premise servers.
- Explore different deployment strategies, ensuring scalability and reliability of AI applications.
- Develop Retrieval-Augmented Generation (RAG) pipelines to enhance the performance and accuracy of generative models by integrating retrieval mechanisms.
- Learn to seamlessly incorporate Huggingface's pre-trained models into Langchain applications, leveraging their powerful NLP capabilities.
- Customize and fine-tune Huggingface models to fit specific application requirements and use cases.
- Work on real-world projects that illustrate the application of generative AI in various domains, such as chatbots, content generation, and data augmentation.
Best For
- Developers and data scientists looking to build production-ready LLM applications.
- Engineers wanting to master RAG pipelines using LangChain and Hugging Face frameworks.
- AI practitioners who need practical, hands-on experience with model deployment and fine-tuning.
- Tech professionals aiming to automate workflows using custom Large Language Model architectures.
Not For
- Absolute beginners with no foundational understanding of Python programming or machine learning concepts.
- Individuals looking for a high-level conceptual overview without deep-dive technical coding.
- Those strictly interested in AI ethics or policy rather than the practical implementation and architecture of models.
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