Applied Machine Learning | Columbia University COMS W4995

Practical machine learning techniques, from data preprocessing to model deployment. Hands-on experience with popular libraries and real-world datasets.

Introduction

This course covers the practical aspects of applying machine learning techniques to real-world problems. It focuses on the entire machine learning pipeline, from data preprocessing and visualization to model selection, evaluation, and deployment.

Highlights

• Hands-on experience with popular machine learning libraries and tools, such as scikit-learn, matplotlib, and pandas
• In-depth coverage of supervised learning techniques, including linear models, decision trees, random forests, and gradient boosting
• Emphasis on model validation, calibration, and handling imbalanced data
• Exposure to a variety of real-world datasets and machine learning applications

Recommendation

This course is recommended for students who have a strong foundation in machine learning and are interested in applying their knowledge to practical problems. It is suitable for both beginners and experienced practitioners looking to enhance their skills in applied machine learning.