As businesses gather vast amounts of data, machine learning is becoming an increasingly valuable tool for utilizing data to deliver cutting-edge predictive models that support informed decision-making.

In this course, you will work on a data science project with a realistic dataset to create actionable insights for a business. You’ll begin by exploring the dataset and cleaning it using pandas. Next, you will learn to build and evaluate logistic regression classification models using scikit-learn. You will explore the bias-variance trade-off by examining how the logistic regression model can be extended to address the overfitting problem. Then, you will train and visualize decision tree models. You’ll learn about gradient boosting and understand how SHAP values can be used to explain model predictions. Finally, you’ll learn to deliver a model to the client and monitor it after deployment.

By the end of the course, you will have a deep understanding of how data science can deliver real value to businesses.