Advanced 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.

Lessons and practices

Lesson 1: Mastering Predictive Modeling with SVM in Python

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

Lesson 2: Understanding and Applying Decision Tree Regression

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

Lesson 3: Mastering Predictions with Random Forest Classifier in Python

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

Lesson 4: Diving into Neural Networks with Python

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

Lesson 5: Balancing Act: Overfitting and Underfitting in Machine Learning Models

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

Interested in this course? Learn and practice with Cosmo!

Practice is how you turn knowledge into actual skills.