AI – Machine Learning Engineer
Course Overview
The AI – Machine Learning Engineer course equips learners with essential skills in AI, machine learning, and big data analytics. Through hands-on experience, participants will develop, evaluate, and deploy algorithmic models, manage data pipelines, and apply statistical analysis. The course also emphasizes product engineering, project management, and effective communication, preparing graduates for roles in diverse industries such as technology, healthcare, finance, and robotics, while fostering a sustainable and inclusive workplace environment This comprehensive course is designed to equip learners with the essential skills and knowledge to excel in various roles within the rapidly growing fields of Artificial Intelligence (AI) and Big Data Analytics. Participants will explore the application of AI and Machine Learning across multiple industries, including technology, finance, healthcare, retail, government, and more. By the end of this course, participants will be prepared for roles such as Data Scientist, Machine Learning Engineer, AI Data Analyst, Big Data Engineer, Robotics Scientist, and AI Engineer.
The Duties of AI – Machine Learning Engineer
- Describe the use cases of AI & Big Data Analytics in various industries and define the various roles under this occupation.
- Describe product engineering concepts such as translating requirements into products and ensuring their timely delivery.
- Define basic statistical concepts used for analysis such as measures of central tendency like mean, median, or mode, or statistical anomalies like missing values, bias, or outliers.
- Use development tools, frameworks, platforms, libraries, and packages to develop software code.
- Evaluate the running time and memory consumption of the model and modify it to suit the speed and memory constraints of the system.
- Develop software code that can support the deployment of algorithmic models based on the requirements and constraints of the system.
- Plan project schedules and timelines based on the nature of work.
- Demonstrate effective communication and collaboration with colleagues.
- Apply measures to maintain standards of health and safety at the workplace.
- Use different approaches to effectively manage and share data and information.
- Develop strong relationships at the workplace through effective communication and conflict management.
- Identify best practices to maintain an inclusive, environmentally sustainable workplace.
