Machine Learning Interview Playbook: Practical Systems and Case Studies
Intermediate18 hours11 sections74 lessonsRated 4.8 out of 5 from 11 ratings
Practice the decisions that come up in machine learning interviews: framing a task, choosing a baseline, evaluating models, debugging failures, and explaining project work with evidence.
What you will be able to do
- Frame an ML interview problem and choose metrics with stated assumptions.
- Design search, feed, recommendation, and detection systems end to end.
- Handle delayed labels, rare events, and calibration in applied cases.
- Explain trade-offs and failure cases in complete mock interview answers.
Who this course is for
Candidates preparing for applied machine learning interviews who want to practice framing, evaluation, and case discussions.
Helpful to know first
- Core machine learning concepts: supervised learning, common model families, and evaluation metrics.
Course content
11 sections 74 lessons 18 hours
Frame an ML interview problem, choose evaluation metrics, and explain practical techniques with clear assumptions and examples.
- How Does This Course Help in ML Interviews?10 min
- Setting Up a Machine Learning System15 min
- Performance and Capacity Considerations10 min
- Training Data Collection Strategies18 min
- Online Experimentation10 min
- Embeddings11 min
- Transfer Learning10 min
- Model Debugging and Testing12 min
- Practical ML Techniques/Concepts10 min
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Rated 4.8 out of 5 from 11 ratings
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