MACHINE LEARNING MODELS​

Course Description

The Machine Learning Models course provides an in-depth understanding of tree-based and ensemble learning techniques, focusing on models such as Decision Trees, Random Forests, and Gradient Boosting. It is designed to help learners master how these models work and how they can be applied to solve real-world problems. Participants will explore key concepts including entropy, information gain, and the CART algorithm, along with techniques to address challenges like overfitting and high variance. The course also emphasizes practical skills such as hyperparameter tuning, feature importance analysis, and model interpretability. Using tools like Python’s Scikit-learn and SHAP (Shapley values), learners will gain hands-on experience in building, evaluating, and optimizing machine learning models for classification and regression tasks. This program prepares participants to apply robust, interpretable models in data science and machine learning applications.

Course Details

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