How to Learn ML in 2026 interview (vickiboykis.com)

In a recent interview with Yacine from "Deep Learning with Yacine," Vicki Boykis, an accomplished machine learning engineer, shared her insights on starting a career in machine learning in 2026. Boykis, who transitioned from a self-taught background in economics and analytics to her current role at Malachyte—a startup focusing on real-time personalization for e-commerce—discussed her journey of continuously acquiring skills by observing and learning from colleagues in the field. This reflects a broader trend in the AI/ML community where individuals come from diverse backgrounds and emphasize practical learning. Boykis stressed the importance of foundational knowledge in machine learning, advocating for a blend of traditional learning methods and modern tools. She highlighted the significance of understanding the fundamentals—such as linear algebra and foundational ML concepts—despite the increasing accessibility of APIs and advanced models. By emphasizing the importance of engaging deeply with core concepts, she recommends using resources like books and hands-on projects to internalize key skills. This approach not only builds a stronger foundation but also prepares aspiring ML engineers to adapt to rapidly evolving technologies and maintain a solid grasp of the underlying principles driving innovation in the field.