This intermediate path introduces core machine learning workflows using Python tools such as scikit-learn, TensorFlow, and Pandas. You will work with common datasets to practice preparing data, building models, evaluating results, and improving performance. The path begins with foundational supervised learning models, including regression, classification, and decision trees, then moves into data cleaning, preprocessing, and feature engineering. You will learn how thoughtful data preparation and feature design can improve model quality. Later courses focus on model optimization techniques such as hyperparameter tuning, regularization, and ensemble methods. You will also develop and evaluate neural networks in TensorFlow, gaining experience with deep learning concepts and model architecture choices.
