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Module 9
Model Tuning and Unsupervised Learning
1
K-Fold Cross-Validation for Robust Model Evaluation
Apply k-fold cross-validation to obtain robust model performance estimates.
2
Hyperparameter Tuning with GridSearchCV
Perform hyperparameter tuning for a model using GridSearchCV.
3
Gradient Boosting with Scikit-learn
Describe the gradient boosting algorithm and train a model using Scikit-learn.
4
Understanding K-Means Clustering
Explain the K-Means clustering algorithm for unsupervised grouping.
5
K-Means Clustering with Scikit-learn: Finding Optimal Clusters
Apply K-Means to a dataset using Scikit-learn and find the optimal number of clusters.
6
PCA: The Intuition
Explain the intuition behind Principal Component Analysis (PCA) for dimensionality reduction.
7
PCA with Scikit-learn: Explained Variance Analysis
Apply PCA using Scikit-learn and analyze the explained variance.
8
Scikit-learn Pipelines: Streamlining ML Workflows
Build a machine learning pipeline in Scikit-learn to combine preprocessing and model training.
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Advanced Supervised Learning Models
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Object-Oriented Programming and PyTorch Basics