Foundational Machine Learning Models with Sklearn
Learn fundamental machine learning models with Sklearn, centered on the Iris Dataset. This course covers key algorithms like linear and logistic regression, and decision trees. Master implementation, evaluation, and optimization to pave the way for advanced machine learning concepts.
Lessons and practices
Inspecting the Shape of the Iris Dataset
Loading the Iris Dataset from Sklearn
Splitting the Dataset into Training and Testing Data
Fixing the Dataset Splitting Code
Implementing a Linear Regression Model with 2 Features
Model fitting and evaluation
Assess Model Performance on Test Data
Splitting the Dataset into Training and Testing Data
Splitting the Dataset into Training and Testing Data
Generating Predictions using Logistic Regression Model
Tune the Logistic Regression Model
Implement Logistic Regression model training
Implement Decision Tree with Different Splitting Criterion
Add Visualization for Confusion Matrix
Decision Tree Tuning
Calculate Decision Tree Model Accuracy
Crunching Numbers: Calculating Mean Absolute Error
Precision in Logistic Regression Models
Evaluating Accuracy of Decision Tree Model
Implement Linear Regression on the Iris Dataset
Implementing Logistic Regression Model with Iris Dataset
"Improving Decision Tree Model with Parameter Tuning"
Adjust Hyperparameters and Optimize the Same Model
Adjust Hyperparameters and Optimize with RandomSearchCV
Enhance Decision Tree Classifier Performance
Enhance Decision Tree Performance with RandomizedSearch
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