This course provides a step-by-step roadmap to become a machine learning engineer in 2026, taught by Egor Howell. It is ideal for beginners, aspiring ML engineers, and data science enthusiasts looking to strategically enter the field and land high-paying roles.

The course begins by outlining the key skills required for ML engineers, including mathematics, programming, data structures, and core machine learning concepts. Egor emphasizes the importance of hands-on projects that demonstrate practical knowledge, and provides guidance on the types of projects that give candidates an unfair advantage during job applications.

Learners are guided on portfolio building, resume creation, and interview preparation, with concrete examples of resumes that have landed $100K+ offers. The course also highlights common pitfalls, explaining why some data scientists get job offers faster than others, and how to avoid mistakes in learning and application.

Practical advice on career planning, project selection, and skill prioritization ensures learners make progress efficiently. By the end of this course, learners will have a clear, actionable roadmap to become a competent ML engineer, build an impressive portfolio, and secure competitive roles in top companies.