Predictive Modeling with PythonAdvanced Machine Learning Models for Prediction

As you become more proficient with regression models, this course will introduce you to more advanced models available in the Scikit-Learn library. Explore popular machine learning algorithms, including Support Vector Machines, decision trees, random forest and neural networks.

Running the SVM Regressor to Predict Housing Prices

Switching the SVM Kernel to 'Polynomial'

Implement Training and Prediction for SVM Regressor

Navigating the SVM Galaxy: Predicting California Housing Prices

California Dreamin': Predicting House Values with Decision Trees

Adjusting Tree Depth for Better Predictions

Debugging the Decision Tree Regressor

Planting the Decision Tree

Building a Decision Tree Regressor from Cosmic Dust

Predicting Housing Values with Random Forest Regressor

Adjusting the Forest: Tuning the Number of Trees in Random Forest Regressor

Random Forest Regressor Code Review

Building the Forest for Future Predictions

California Dreaming: Implement Your Own Random Forest Regressor

Predicting House Prices with Neural Networks

Activating the Network: A Neural Adjustment

Neural Network Regression Challenge

Adjusting the Neural Network's Hidden Layers

Building a Neural Network for Regression from Scratch

Exploring Overfitting and Underfitting with SVM in Python

Balancing the SVM Regressor: Regularization with 'C'

Adjusting Parameter for Model Optimization

Tuning the SVM Regressor's Complexity

SVM Regressors: Balancing Complexity and Generalization