Validate your Data Science skills with 200 practice scenarios on TensorFlow, Regression, and Ensemble Methods.
Sub Category
- Data Science
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Objectives
- Evaluate Regression models (RMSE, MAE, R-Squared) and Classification models (Precision, Recall, F1-Score, ROC-AUC) to determine predictive accuracy.
- Prevent overfitting and the Bias-Variance tradeoff by implementing robust validation techniques like K-Fold Cross-Validation and Regularization (L1/L2).
- Preprocess raw data for algorithms through Feature Engineering, Scaling (MinMaxScaler, StandardScaler), and handling imbalanced classes (SMOTE).
- Optimize deep learning architectures using TensorFlow and Keras, while tuning hyperparameters for Ensemble Methods (Random Forests, XGBoost).
Pre Requisites
- A foundational understanding of Python programming and basic statistical concepts. Familiarity with importing libraries like Pandas, Scikit-Learn, or TensorFlow is highly recommended to visualize the scenarios.
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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