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Explore advanced ChatGPT prompting techniques, from freeform to structured formats. Learn scientific principles, master fundamentals, and acquire skills in chain-of-thought and skeleton prompts for optimal results with large language models.
Explore how generative AI revolutionizes data analytics, streamlining tasks, transforming workflows, and ensuring ethical practices. Gain practical insights and real-world applications for data professionals.
Learn how recommendation systems are designed, from baseline models and performance metrics to industry-scale considerations.
Combine AI and business intelligence through value mapping and feasibility analysis, with a live PDF-to-structured-data demonstration.
Discover how to build scalable multi-agent AI systems using LangChain, exploring design patterns, orchestration techniques, and production-ready architectures beyond single agents.
Build interactive KNIME data apps that let users explore analytics through parameterized web interfaces, using widgets and visualization nodes.
Use KNIME’s visual workflows to classify review sentiment and extract topics from unstructured text without coding.
Use Python, pandas, and Plotly to turn data into interactive charts that communicate a clear message to stakeholders.
Explore how Kubernetes and OpenShift support MLOps workflows, from Jupyter-based model development to deploying a machine-learning prediction application.
Explore tokenization in NLP, from basics to advanced techniques. Learn about word embeddings, text splitting, BytePair Encoding, and token vectorization for enhanced language model performance.
Explore recommender systems' impact on digital strategies, from basic models to advanced approaches. Learn implementation strategies and address deployment challenges.
Explore essential data preparation techniques for LLM development. Learn to clean, filter, and transform datasets for pre-training or fine-tuning, accelerating your AI projects with collaborative tools.
Discover how to build advanced LLM applications using LangGraph for multi-agent collaboration, enabling specialized task handling and improved workflow management through practical demonstrations and hands-on exercises.
Gain insights into AI fundamentals, from NLP and machine learning to ethical considerations, while exploring real-world applications, security practices, and implementation strategies.
Discover how to prepare AI-ready data for reliable generative AI applications, from understanding data architecture to implementing observability practices that detect and resolve incidents in enterprise environments.
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