Lilly Tech Systems
Python for ML Interviews
Practical Python coding challenges with complete solutions — the exact type of problems asked in machine learning interviews. Master NumPy, Pandas, Scikit-Learn, PyTorch, and tricky data manipulation puzzles under interview conditions.
Your Learning Path
Each lesson contains real interview challenges with full solutions. Follow in order or jump to the library you need to practice.
1. Python Coding in ML Interviews
What to expect, tools allowed, coding style expectations, time management strategies, and how interviewers evaluate your Python fluency.
2. NumPy Interview Challenges
10 challenges covering array operations, broadcasting, matrix multiplication, vectorization, and distance calculations — all with complete solutions.
3. Pandas Interview Challenges
10 challenges on groupby, merge, pivot, window functions, time series, and data cleaning — the pandas skills interviewers test most.
4. Scikit-Learn Challenges
10 challenges on pipelines, custom transformers, cross-validation, grid search, and feature selection using the sklearn API correctly.
5. PyTorch Interview Challenges
8 challenges on custom datasets, custom layers, training loops, loss functions, and model debugging in PyTorch.
6. Data Manipulation Puzzles
10 tricky data problems: deduplication strategies, merge edge cases, complex aggregation patterns, and performance optimization techniques.
7. Practice Problems & Tips
Timed challenges, optimization tips, common pitfalls, and an interactive FAQ accordion for last-minute interview preparation.
What You'll Learn
By the end of this course, you will be able to:
Master NumPy for ML
Write vectorized operations, broadcasting patterns, and matrix computations that interviewers expect you to know cold.
Wrangle Data with Pandas
Handle messy datasets, complex joins, time series, and window functions under time pressure with clean, idiomatic code.
Use Sklearn Professionally
Build production-quality pipelines, custom transformers, and proper cross-validation workflows that demonstrate senior-level skills.
Code PyTorch from Scratch
Implement custom datasets, layers, training loops, and debug model issues — the deep learning skills that set you apart.