Building Recommender Systems with Machine Learning and AI

For Building Recommender Systems with Machine Learning and AI, applications close Anytime; tuition is 49.

The Building Recommender Systems with Machine Learning and AI program is for students who want to learn about machine learning and AI. It's a non-degree program that lasts several weeks and is taught in English.

The curriculum covers machine learning and AI topics, including how to build recommender systems. Students will learn skills like data analysis and programming. They will also get hands-on experience with projects and exercises.

After completing this program, students can work as Data Scientists, Machine Learning Engineers, AI Researchers, or Software Developers. They can find jobs in tech companies, research institutions, or consulting firms.

Packt is a specialised provider of practical technology learning resources based in Birmingham, serving developers, IT professionals, and tech enthusiasts worldwide. Since 2004 the organisation has focused on producing accessible books, eBooks, and video courses that translate emerging tools and methodologies into usable skills. Packt’s catalog covers software development, data science, machine learning, cloud computing, and modern infrastructure topics that professionals need to stay productive and competitive.

The learning experience at Packt is shaped by concise, example-driven content designed for hands-on application rather than theory alone. Readers and learners can follow tutorials, code samples, and project-based guides that are easy to integrate into workplace tasks or personal projects. For international learners, Packt’s digital delivery and global distribution make it simple to access material across time zones, with many titles suitable for self-paced professional development and team training.

Packt’s resources complement formal education and workplace training by offering targeted upskilling pathways and reference material for real-world challenges. Individuals planning career transitions or skill refreshers will find clear, actionable guidance and code examples they can adapt immediately. Prospective users should browse topic collections, trial sample chapters or videos, and consider subscription or bundle options that align with their learning goals and preferred pace.