Lilly Tech Systems
NumPy Coding Challenges
The NumPy challenges that Google, Meta, and Amazon ask in ML engineer interviews. Each problem comes with a naive loop-based solution and a fully vectorized NumPy solution — the kind interviewers want to see. Master array operations, matrix math, statistical computations, and ML building blocks in pure NumPy.
Your Learning Path
Follow these lessons in order for a complete preparation for NumPy-based ML coding interviews, or jump to any topic.
1. NumPy for ML Interviews
Why NumPy matters for ML roles, the vectorization philosophy, common interview patterns, and how top companies evaluate NumPy fluency.
2. Array Operations
6 challenges: reshape and transpose, broadcasting arithmetic, fancy indexing, boolean masking, stacking arrays, and splitting datasets.
3. Matrix Mathematics
6 challenges: dot product, matrix multiplication, matrix inverse, determinant computation, eigenvalue decomposition, and singular value decomposition.
4. Statistical Operations
6 challenges: mean/std/variance along axes, percentile computation, correlation matrix, feature normalization, z-score standardization, and covariance matrix.
5. ML Implementations in NumPy
5 challenges: numerically stable softmax, cross-entropy loss, gradient descent step, batch normalization, and cosine similarity matrix.
6. Distance & Similarity
5 challenges: Euclidean distance, Manhattan distance, cosine similarity, pairwise distance matrix, and K-nearest neighbor prediction.
7. Performance & Vectorization
5 challenges: replacing Python loops with vectorized ops, einsum mastery, memory-efficient operations, broadcasting tricks, and advanced indexing.
8. Quick Reference & Tips
NumPy cheat sheet for interviews, common patterns and idioms, performance tips, and frequently asked questions with detailed answers.
What You'll Learn
By the end of this course, you will be able to:
Think in Vectors, Not Loops
Replace Python for-loops with vectorized NumPy operations that run 10-100x faster. This is the single most tested skill in ML coding interviews.
Implement ML Primitives
Build softmax, cross-entropy, batch normalization, gradient descent, and distance metrics from scratch using only NumPy.
Master Broadcasting & Indexing
Use broadcasting rules, fancy indexing, and boolean masking to write concise, efficient array computations that interviewers expect.
Optimize for Performance
Use einsum, memory views, contiguous arrays, and vectorized patterns to write production-quality NumPy code.