Classification with Python and Scikit-Learn (Audio Course)

Course Description: Decision Trees & Random Forests with Python and Scikit-Learn Machine Learning Library

Unlock the power of Decision Trees and Random Forests with this hands-on course using Python and Scikit-Learn! Whether you’re a beginner in machine learning or looking to deepen your knowledge of ensemble learning, this course will provide you with a solid foundation.

You’ll start with the fundamentals of decision trees, understanding how they split data, measure impurity, and make predictions. Then, we’ll explore Random Forests—an ensemble learning technique that improves accuracy and reduces overfitting. You’ll learn how to fine-tune hyperparameters, interpret feature importance, and handle imbalanced datasets.

Through practical exercises and real-world datasets, you’ll implement decision trees and random forests using Python’s powerful Scikit-Learn library. You’ll visualize decision boundaries, optimize model performance, and compare results with other machine learning algorithms.

By the end of the course, you’ll be able to:
Understand and implement Decision Trees from scratch
Train and fine-tune Random Forest models
Perform feature selection and interpret model results
Optimize hyperparameters for better performance
Apply these techniques to real-world datasets

No prior machine learning experience is required—just basic Python knowledge! Enroll now and take your data science skills to the next level with Decision Trees and Random Forests! You are welcome to this new course !