Ace data science interviews with 200 questions on TensorFlow, CNNs, Hyperparameter Tuning, and Evaluation Metrics.
Sub Category
- Data Science
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Objectives
- Differentiate between Supervised, Unsupervised, and Reinforcement Learning algorithms to choose the right model for complex data problems.
- Architect and evaluate deep learning networks using TensorFlow and Keras, configuring appropriate loss functions and activation layers.
- Master Scikit-Learn pipelines to prevent data leakage and utilize RandomizedSearchCV for highly efficient hyperparameter tuning.
- Calculate and apply the correct evaluation metrics (Precision, Recall, F1-Score, RMSE) based on the specific business context of the model.
Pre Requisites
- A firm grasp of Python programming and fundamental mathematics (linear algebra and basic calculus). Familiarity with Jupyter Notebooks and basic data manipulation using Pandas and NumPy is highly recommended.
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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