Global Certificate in AI Inspection Excellence
-- ViewingNowThe Global Certificate in AI Inspection Excellence is a comprehensive course designed to equip learners with essential skills in AI inspection. This course highlights the importance of AI in various industries, including manufacturing, healthcare, and transportation, by teaching the latest AI technologies and inspection methods.
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⢠Fundamentals of Artificial Intelligence (AI): An introduction to AI, including its history, concepts, and primary applications. This unit will cover AI terminology, the difference between weak and strong AI, and the AI development process.
⢠Machine Learning (ML): An exploration of ML, a subset of AI, focusing on supervised and unsupervised learning techniques, reinforcement learning, and deep learning. This unit will also cover data preprocessing, feature selection, and model evaluation.
⢠Computer Vision (CV): An overview of CV, a field within AI, focusing on image and video processing, object detection, and recognition. This unit will cover CV techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
⢠Natural Language Processing (NLP): An introduction to NLP, a subset of AI, focusing on text processing, sentiment analysis, and machine translation. This unit will cover NLP techniques, such as tokenization, stemming, and part-of-speech tagging.
⢠AI Ethics and Regulations: A discussion on the ethical and regulatory considerations surrounding AI, including data privacy, bias, transparency, and accountability. This unit will also cover global AI regulations and their impact on AI development.
⢠AI Deployment and Maintenance: An exploration of AI deployment and maintenance strategies, including DevOps, MLOps, and AIOps. This unit will cover best practices for deploying AI models in production, monitoring, and maintaining AI systems.
⢠AI Inspection Techniques: An overview of AI inspection techniques, including testing, validation, and verification. This unit will cover AI model testing frameworks, test data generation, and test case design.
⢠Quality Assurance (QA) and AI: A discussion on the role of QA in AI development, including test automation, continuous integration, and continuous delivery. This unit will also cover QA best practices for AI projects.
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