The Artificial Intelligence Master’s Program covers the crucial skills you need for a successful career in artificial intelligence (AI). As you undertake your AI engineer training, you’ll master the concepts of deep learning, machine learning, natural language processing (NLP), plus the programming languages needed to excel in an AI career with exclusive training and certification from IBM.
You will learn how to design intelligent models and advanced artificial neural networks and leverage predictive analytics to solve real-time problems in this course, in collaboration with IBM.
With the demand for Artificial Intelligence in a broad range of industries such as banking and finance, manufacturing, transport and logistics, healthcare, home maintenance, and customer service, the Artificial Intelligence course is well suited for a variety of profiles like:
- Developers aspiring to be an ‘Artificial Intelligence Engineer’ or Machine Learning engineers
- Analytics managers who are leading a team of analysts
- Information architects who want to gain expertise in Artificial Intelligence algorithms
- Graduates looking to build a career in Artificial Intelligence and Machine Learning
Key Learning Objectives:
- Learn about the major applications of Artificial Intelligence across various use cases across various fields like customer service, financial services, healthcare, etc.
- Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, and tracking.
- Gain the ability to apply Artificial Intelligence techniques for problem-solving and explain the limitations of current Artificial Intelligence techniques.
- Master the skills and tools used by the most innovative Artificial Intelligence teams across the globe as you delve into specializations, and gain experience solving real-world challenges.
- Design and build your own intelligent agents and apply them to create practical Artificial Intelligence projects including games, Machine Learning models, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision-making functions, and more.
- Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline.
- Understand and master the concepts and principles of Machine Learning, including its mathematical and heuristic aspects.
- Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces.
- Learn to deploy deep learning models on Docker, Kubernetes, and in serverless environments (cloud)
- Understand the fundamentals of Natural Language Processing using the most popular library; Python’s Natural Language Toolkit (NLTK).
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