Applied Machine Learning

Machine learning is transforming how organizations make decisions, build products, and create value from data. Yet moving from data analysis to reliable, production-ready predictive models requires a distinct set of skills — theoretical understanding, practical implementation, and the critical ability to evaluate and improve model performance.

The Certificate of Open Studies (COS) in Applied Machine Learning offers a structured, hands-on pathway through the full machine learning workflow: from supervised learning foundations and model optimization to advanced non-linear classifiers and deep learning architectures. Participants develop practical fluency using Python’s machine learning stack — Scikit-learn, TensorFlow, and Keras — applied across real-world predictive tasks. Combining four complementary Microcredentials, the program equips participants with the technical expertise and best practices needed to tackle real-world machine learning challenges across industries.

Identify supervised learning scenarios as regression or classification tasks. Construct suitable feature spaces through feature engineering, selection, and data preprocessing. Explain the theoretical foundations of core machine learning concepts including gradient descent and the bias-variance trade-off Implement end-to-end machine learning pipelines for classification and regression tasks using Scikit-learn Evaluate a range of supervised learning algorithms, including linear models, k-nearest neighbors, decision trees, support vector machines, and ensemble methods, using appropriate metrics. Diagnose model failures such as overfitting, underfitting, and ill-conditioning through hyperparameter tuning and regularization. Build neural network architectures using Keras and TensorFlow, selecting the appropriate model for a given data type. Leverage pre-trained models to improve generalization and address tasks with limited datasets.

Nongraded

Professionals working with data who want to use Python to build predictive models and expand their capabilities in machine learning and AI. The program is also suitable for data analysts and quantitative analysts looking to integrate machine learning models into their workflow, research scientists seeking more advanced, custom predictive frameworks, and software engineers transitioning into machine learning and AI with the long-term goal of building real-world applications.