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Intro to Model Optimization in Machine Learning

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Overview

Delve into machine learning fundamentals using the Wisconsin Breast Cancer Dataset. This course focuses on key ML techniques like data exploration, model tuning, and evaluation. Master hyperparameter tuning, regularization, and ensemble methods through practical exercises to boost your predictive models' accuracy and reliability.

Syllabus

Unit 1: Exploring the Wisconsin Breast Cancer Dataset

Getting Acquainted with the Breast Cancer Dataset
Retrieving the First 5 Features of the Dataset
Debugging Class Counts in Dataset
Unveiling Descriptive Statistics of Dataset

Unit 2: Hyperparameter Tuning in Logistic Regressions

Correcting Data Scaling Issues
Tuning Hyperparameters with GridSearchCV
Navigating the Hyperparameter Space

Unit 3: Optimizing Decision Trees with Hyperparameter Tuning

Widening the GridSearchCV Parameter Range
Sailing Through Decision Tree Hyperparameters
Navigating the GridSearch Space
Exploring the Decision Tree Parameter Space

Unit 4: Regularization Techniques in Machine Learning: Enhancing Model Generalization

Switching to L2 Regularization in Logistic Regression
Mastering L2 Regularization in Logistic Regression
Cracking the Regularization Code
Mastering L1 Regularization Performance
Applying L1 Regularization Mastery

Unit 5: Elevating Predictive Models with RandomForest and GradientBoosting Techniques

Adjusting RandomForest Hyperparameters for Better Accuracy
Prepare Data for Your Gradient Boosting Classifier
Hyperparameters and the Art of Boosting
Boost Your Classifier with Gradient Boosting

Unit 6: Mastering Model Evaluation: Performance Metrics & Selection in Machine Learning

Scaling Features for Improved Accuracy
Performance Boost with Data Scaling
Deploying a Complete Machine Learning Pipeline

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