Artificial intelligence (AI) and machine learning (ML) are indispensable in solving business problems. This course, part of the Certified Artificial Intelligence Practitioner (CAIP) certification, provides a comprehensive introduction to AI/ML applications in business settings. Learners will gain insights into identifying suitable AI/ML solutions, formulating machine learning approaches, and selecting the right tools for specific business problems. The course emphasizes the significance of data privacy and ethical practices, offering techniques for addressing ethical challenges in AI/ML projects. Throughout the modules, learners will explore the data hierarchy, big data, data mining, and the machine learning workflow. They will also delve into the selection of appropriate tools, data privacy laws, protecting data privacy, and promoting ethical practices in AI/ML projects.
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This course comprises four modules that cover a wide range of topics, including applying AI and ML to business problems, selecting appropriate tools for machine learning projects, promoting data privacy and ethical practices, and applying the knowledge gained.
This module provides an overview of the data hierarchy, big data, data mining, and the general machine learning workflow. Learners will also explore the differences between traditional programming and machine learning, supervised and unsupervised learning, and the importance of ethical considerations in AI/ML projects.
Learners will gain insights into new tools and technologies, hardware requirements, cloud platforms, open source and proprietary AI tools, GPU platforms, and guidelines for configuring a machine learning toolset. The module emphasizes the selection of appropriate tools for machine learning projects.
This module focuses on data privacy laws, privacy by design, data privacy principles, compliance with data privacy laws and standards, data sharing, and ethical considerations in AI/ML projects. Learners will explore guidelines for protecting data privacy and promoting ethical practices in machine learning projects.
Applying the knowledge gained from the previous modules, learners will reflect on what they've learned and apply the concepts to real-world business problems. This module provides a practical application of the skills and knowledge acquired throughout the course.
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