Lilly Tech Systems
ML Coding Interview
Crack machine learning coding interviews at top tech companies. Practice real interview questions with complete Python solutions, learn what interviewers evaluate, and build ML algorithms from scratch using only NumPy and core Python.
Your Learning Path
Follow these lessons in order to prepare for ML coding interviews, or jump to any topic you need to practice.
1. What to Expect in ML Coding Rounds
Interview format, tools allowed, evaluation criteria, common mistakes candidates make, and how to structure your approach for maximum impact.
2. Implement Linear & Logistic Regression
Build linear and logistic regression from scratch with NumPy. Gradient descent, loss functions, regularization, and complete interview solutions.
3. Implement Decision Trees & Random Forest
Information gain, Gini impurity, recursive splitting, pruning strategies, and building ensemble methods from scratch.
4. Implement K-Means & KNN
Distance metrics, initialization strategies, convergence criteria, and KNN classifier — all implemented from scratch with interview tips.
5. Implement Neural Networks
Forward pass, backpropagation, activation functions, SGD optimizer — build a full MLP from scratch with NumPy. The hardest interview question, solved.
6. Data Processing Challenges
Feature engineering, handling missing values, encoding categoricals, normalization — real pandas challenges with complete solutions.
7. Implement Evaluation Metrics
Precision, recall, F1, AUC-ROC, confusion matrix, and cross-validation — all implemented from scratch with interview context.
8. Practice Problems & Tips
10 timed practice problems, debugging ML code challenges, frequently asked interview questions, and an interactive FAQ accordion.
What You'll Learn
By the end of this course, you will be able to:
Implement ML from Scratch
Build linear regression, logistic regression, decision trees, K-Means, KNN, and neural networks using only NumPy — the exact skill interviewers test.
Ace Coding Rounds
Know the format, time constraints, and evaluation rubric used at Google, Meta, Amazon, and other top companies for ML coding interviews.
Debug ML Code
Spot common bugs in ML implementations: shape mismatches, gradient issues, data leakage, and numerical instabilities that trip up candidates.
Explain Your Approach
Communicate your thought process clearly while coding — the soft skill that separates strong hires from borderline candidates.