Programming Fundamentals for AI
Description
Core programming skills in Python, R, and essential software engineering practices required for AI development and implementation.
Learning Objectives
Learners will develop proficiency in Python programming for AI applications, understand data structures and algorithms, work with AI libraries and frameworks, implement version control and collaborative development practices, and apply software engineering principles to AI project development.
Topics (7)
Software testing methodologies adapted for AI systems, including unit testing for AI components, integration testing for ML pipelines, model validation, and quality assurance practices for AI applications.
Comprehensive Python programming covering syntax, data types, control structures, functions, object-oriented programming, and essential libraries for scientific computing and AI development.
Study of data structures including arrays, linked lists, stacks, queues, trees, graphs, and hash tables, along with algorithms for searching, sorting, and optimization used in AI systems.
Object-oriented programming concepts including classes, inheritance, polymorphism, encapsulation, and common design patterns used in AI software architecture and development.
Version control systems with focus on Git, collaborative development workflows, code review processes, and project management tools essential for AI team development and deployment.
Database design principles, SQL programming, NoSQL databases, data modeling, and data pipeline development for managing large-scale data in AI applications and machine learning workflows.
Cloud computing platforms for AI deployment, containerization with Docker, orchestration with Kubernetes, CI/CD pipelines, and DevOps practices for scalable AI application development and deployment.