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Courses tagged 'Artificial Intelligence'
tagged with 'Artificial Intelligence'
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Artificial Intelligence Advance
Advanced AI solutions are crucial in real-world systems, especially within enterprises. Integration and operational practices, including MLOps and AI lifecycle management, are essential for effective deployment and maintenance. Addressing challenges such as data drift and latency ensures the models perform optimally after deployment.
Diljeet Singh
CBSE 11 AI (Artificial Intelligence)
Natural Language Processing (NLP) is a vital subfield of Artificial Intelligence that enables interaction between computers and humans using natural language. It consists of two primary components: Natural Language Understanding (NLU), which involves comprehending language, and Natural Language Generation (NLG), which converts data into human language. Despite its applications in areas like chatbots and sentiment analysis, NLP faces challenges such as ambiguity and sarcasm, necessitating the use of libraries like NLTK and spaCy to aid implementation.
Abraham
AI Course Fundamental
The chapter explores the future of artificial intelligence, contrasting narrow AI with artificial general intelligence (AGI), and discusses emerging trends such as explainable AI, edge AI, and automated machine learning. It outlines various career paths and research areas in AI, emphasizing the transformative potential of AI technologies and the importance of ethical considerations. It concludes by highlighting the need for adaptability in a rapidly evolving field.
Diljeet Singh
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Prompt Engineering fundamental course
The capstone project emphasizes the development of a prompt toolkit that consolidates knowledge of prompt design and addresses real-world challenges. By choosing from various tracks, learners are tasked with creating reusable prompt templates, evaluating their effectiveness, and demonstrating adaptability across contexts. The focus on usability, ethical guidelines, and iterative improvement encourages a comprehensive understanding of prompt creation.
Diljeet Singh
CBSE 12 AI (Artificial Intelligence)
Evaluating AI models is crucial for understanding their performance in real-world scenarios, including checking predictions, error rates, and ensuring fairness. Various methodologies such as confusion matrices, evaluation metrics, cross-validation, and ROC curves provide frameworks to assess model quality. These techniques not only help in selecting the best-performing models but also address issues of bias and fairness in AI applications.
Abraham
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