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In-depth exploration of NLP with real-world coding exercises and projects
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Practical guidance on deploying AI applications with pre-trained models
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Comprehensive study of cutting-edge tools like Huggingface and OpenAI APIs
Generative AI and natural language processing are reshaping industries, and this course equips you with the skills to excel in these transformative fields. Beginning with foundational concepts, you'll explore the inner workings of NLP techniques like tokenization, embeddings, and transformers. Through hands-on exercises, you'll grasp how to process, analyze, and model textual data effectively.
As the course progresses, you'll unlock the power of pre-trained models and frameworks like Huggingface to tackle tasks such as sentiment analysis, summarization, translation, and question answering. Delve into advanced topics like vector databases, retrieval-augmented generation, and prompt engineering, learning to design sophisticated, AI-driven systems. Real-world projects, including a climate change chatbot, will cement your practical understanding.
By the end of the journey, you'll be adept at implementing and fine-tuning large language models, mastering tools like the OpenAI API, and applying data augmentation techniques. Whether you're developing AI applications or refining your knowledge in NLP, this course is your gateway to innovation in AI.
This course is tailored for data scientists, software engineers, and AI enthusiasts eager to delve into generative AI and NLP. Prior experience with Python and basic machine learning concepts is recommended. Familiarity with data manipulation libraries like pandas and NumPy will be helpful but not mandatory.
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Master tokenization and word embedding techniques for NLP
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Build and fine-tune AI models using Huggingface and OpenAI APIs
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Implement sentiment analysis and text summarization methods
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Design effective prompts to optimize AI model performance
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Utilize vector databases for enhanced data retrieval efficiency
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Apply advanced RAG techniques to integrate external data sources
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