Artificial Intelligence Machine Learning and Data Science Concepts Technologies and Applications

This course aims to help professionals develop and enhance their understanding of the concepts, technologies, and applications of artificial intelligence, machine learning, and data science, which are transforming various domains and industries. The course will cover topics such as:

• What is artificial intelligence, and how does it differ from narrow and general AI?
• What are the different types of AI, and how do they sense, reason, and act?
• What is machine learning, and how does it enable AI to learn from data and experience?
• What is the difference between advanced analytics and artificial intelligence, and what are the four types of data analytics?
• What are the algorithms behind machine learning, and what are the main categories of machine learning: supervised, unsupervised, and reinforcement learning?
• What are the characteristics and challenges of data, which is the fuel for AI, and what are the best practices for data governance?
• What are the components and categories of the data engineering platform, which enables the storage, processing, and analysis of big data?
• What are the opportunities and use cases for AI in various industries, and how can they be mapped using Porter’s value chain model?
• What are some of the successful examples of AI applications using various technologies, such as natural language processing, image recognition, machine learning, etc.?
• How can you generate and prioritize ideas for AI projects using various approaches and tools, such as the AI funnel process and the AI project canvas?
• How can you run AI projects using a systematic approach and framework, such as the machine learning life cycle and the AI machine learning canvas?
• How can you decide when to make or buy AI solutions, and what are the advantages and disadvantages of each option?
• How can you transform your organization to be AI-ready using a strategic cycle and a maturity assessment model?
• What are the skills and competencies required for AI professionals, and what are the best organizational structures to support AI initiatives?
• What are the ethical issues and risks associated with AI, and how can you ensure trustworthy AI using various guidelines and principles?