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Machine Learning & Predictive AI
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Module 1
The Architect's View of a Machine Learning Project
1
Defining Predictive Problems & Success Metrics
Frame a business problem as a predictive modeling task with clearly defined success metrics.
2
Exploratory Data Analysis Fundamentals
Perform exploratory data analysis (EDA) to understand data types, distributions, and relationships.
3
Bias-Variance Tradeoff Explained
Explain the bias-variance tradeoff and its impact on model generalization.
Diagnosing Overfitting and Underfitting
Diagnose overfitting and underfitting using training and validation error curves.
5
K-Fold Cross-Validation for Robust Model Validation
Design a robust model validation strategy using k-fold cross-validation.
Streamlining Machine Learning Workflows with scikit-learn Pipelines
Build a reproducible data processing and modeling workflow using scikit-learn Pipelines and ColumnTransformers.
7
Establishing a Baseline Model
Define a baseline model to establish a performance benchmark.
8
Model Comparison Experiment Design
Design a model comparison experiment to select the best model for a given problem.
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