1. Code Section Medium LeetCode tasks.

2. ML/DS Section ML theory and practice, probability theory, statistics.

3. ML System Design Section Provide a detailed description of developing a new ML product, from data aggregation to deployment, support, and A/B testing.

Classification Evaluation Metrics with description

What is gradient boosting? What happens if we remove one estimator from boosting?

What is the bias–variance tradeoff? Which of classical ML models have high/low bias/variance?

System Design Section: Make a full pipeline for an ML model. The company has a new product. You have to choose a subset of company clients for push notifications such that most clients start to use that product (uplift modeling).