Machine Learning

This course covers the fundamental concepts and applications of artificial intelligence (AI) and machine learning (ML). Students will learn how to build and train machine learning models, use algorithms for predictive analytics, and apply AI techniques to solve real-world problems. Topics include supervised and unsupervised learning, neural networks, deep learning, and natural language processing (NLP). The course is ideal for those looking to work in AI-powered industries such as finance, healthcare, and technology.

Objectives

This course covers the fundamental concepts and applications of artificial intelligence (AI) and machine learning (ML). Students will learn how to build and train machine learning models, use algorithms for predictive analytics, and apply AI techniques to solve real-world problems. Topics include supervised and unsupervised learning, neural networks, deep learning, and natural language processing (NLP). The course is ideal for those looking to work in AI-powered industries such as finance, healthcare, and technology.

Outcomes

This course covers the fundamental concepts and applications of artificial intelligence (AI) and machine learning (ML). Students will learn how to build and train machine learning models, use algorithms for predictive analytics, and apply AI techniques to solve real-world problems. Topics include supervised and unsupervised learning, neural networks, deep learning, and natural language processing (NLP). The course is ideal for those looking to work in AI-powered industries such as finance, healthcare, and technology.

Assessment

No