Practice tests with solutions for ML interviews: supervised, deep learning, metrics, Python, MLOps, system design
Sub Category
- IT Certifications
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Objectives
- Solve timed AI/ML practice tests covering supervised/unsupervised learning, evaluation metrics, and model selection
- Explain bias–variance, overfitting, regularization, and cross‑validation with practical reasoning and examples
- Implement and reason about Python, NumPy, pandas, and scikit‑learn snippets and end‑to‑end ML pipelines.
- Design ML systems: data prep, feature engineering, deployment options, monitoring, metrics, and MLOps trade‑offs.
Pre Requisites
- Familiarity with core ML concepts (supervised/unsupervised learning and common metrics) is helpful.
- computer with internet; optional Jupyter/Colab to try code patterns alongside practice questions.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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