This course provides an introduction to classification, a fundamental technique in supervised machine learning used to predict categorical outcomes. Learn how to build, train, and evaluate predictive models using methods such as logistic regression, decision trees, and powerful ensemble techniques like random forests and gradient boosting.
You’ll gain hands-on experience with essential machine learning practices, including properly splitting data into training and testing sets to avoid overfitting and using techniques like oversampling and undersampling to handle unbalanced datasets. This ensures your models are both accurate and robust when applied to real-world data.
A key focus of the course is on model evaluation using a range of error metrics, helping you compare performance and choose the best model for your data. By the end of the course, you will understand when to use classification versus other supervised learning methods, how to implement and interpret different classification algorithms, and how to use best practices to ensure your models are effective and generalizable.
This course is ideal for aspiring machine learning engineers and data scientists looking to apply classification techniques in practical business scenarios. Whether you’re aiming to predict customer churn, detect fraud, or categorize products, this course will equip you with the skills needed to solve real-world classification problems.
To succeed in the course, you should be comfortable with Python programming and have a foundational understanding of data cleaning, exploratory data analysis, calculus, linear algebra, probability, and statistics.
