Validate your Data Science skills with 200 questions on Scikit-Learn, TensorFlow, Regression, and Neural Networks.
Sub Category
- Data Science
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Objectives
- Evaluate regression models (predicting continuous variables like house prices or energy efficiency) using metrics like RMSE, MAE, and R-squared.
- Build and evaluate robust classification models (like customer churn predictors) focusing on Precision, Recall, and F1-Score.
- Design sequential deep learning models using TensorFlow and Keras, optimizing activation functions (ReLU, Sigmoid) and preventing overfitting with Dropout.
- Process raw datasets effectively through feature engineering, cross-validation, and handling imbalanced data using techniques like SMOTE.
Pre Requisites
- A foundational understanding of Python programming (specifically Pandas and NumPy) and basic statistical concepts. Previous exposure to Kaggle datasets or introductory machine learning libraries 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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