Artificial Intelligence
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COURSE DESCRIPTION
Course Duration 900 Hours
Program Completion: 46 weeks
The AWS Certified AI Practitioner course is designed to provide individuals with the foundational knowledge necessary to understand and utilize artificial intelligence (AI) and machine learning (ML) within the AWS environment. This course is ideal for those new to AI/ML or those looking to validate their skills with an industry-recognized certification.
Job Outlook of Artificial Intelligence Professional
Careers in AI span a wide range of roles, including AI Engineer, Machine Learning Scientist, Data Scientist, Research Scientist, AI Product Manager, and Robotics Engineer. AI skills are applicable in various industries, providing opportunities in technology companies, research institutions, startups, and traditional industries undergoing digital transformation. AI Engineers and Machine Learning Scientists often earn between $100,000 and $150,000 annually, with variations depending on experience, location, and specific role.
Why become an Artificial Intelligence Professional?
AI is one of the fastest-growing fields in technology, with applications across diverse industries such as healthcare, finance, automotive, retail, and more. This growth drives high demand for skilled AI professionals. There are a wide variety of roles available in AI, including AI Engineer, Machine Learning Scientist, Data Scientist, Research Scientist, AI Product Manager, and Robotics Engineer.
OBJECTIVE
The objective of an Artificial Intelligence (AI) course is to provide participants with a comprehensive understanding of AI concepts, techniques, and applications, equipping them with the skills needed to develop, implement, and manage AI solutions.
COURSE CONTENT
- Understand the basic concepts and terminology of AI and ML.
- Learn about different types of ML models and their applications.
- Explore AWS services related to AI and ML, such as Amazon SageMaker, AWS DeepLens, and AWS DeepRacer.
- Understand how to use these services to build, train, and deploy ML models.
- Learn how to prepare and clean data for ML projects.
- Understand feature engineering and its importance in improving model accuracy.
- Gain insights into the process of training ML models.
- Learn about different evaluation metrics to assess model performance.
- Explore case studies and practical applications of AI/ML in various industries.
- Understand the ethical considerations and challenges in AI/ML.
