How Should a Non-CS (Economics) Student Learn Machine Learning?
Analysis
This article presents a common challenge faced by students from non-computer science backgrounds who want to learn machine learning. The author, an economics student, outlines their goals and seeks advice on a practical learning path. The core issue is bridging the gap between theory, practice, and application, specifically for economic and business problem-solving. The questions posed highlight the need for a realistic roadmap, effective resources, and the appropriate depth of foundational knowledge.
Key Takeaways
- •The article highlights the challenges of learning ML for non-CS students.
- •The focus is on bridging the gap between theory and practical application.
- •The author seeks advice on a learning roadmap, resources, and the necessary depth of foundational knowledge.
- •The context is applying ML to economics and business problems.
Reference
“The author's goals include competing in Kaggle/Dacon-style ML competitions and understanding ML well enough to have meaningful conversations with practitioners.”