Machine Learning & Explainability for Data Science

Build a powerful binary classification model to predict job seeker intent! This hands-on course guides you through the entire end-to-end machine learning project lifecycle. You'll gain practical experience in data collection, data exploration, and feature engineering – crucial steps for any successful machine learning endeavor.

We'll explore various model selection techniques and data transformation methods to prepare your data for training. Learn how to effectively train and evaluate your model, ensuring accurate predictions. A key focus is on model explainability, allowing you to understand why your model makes specific predictions. By the end, you'll be able to confidently identify the key features that indicate someone is actively searching for a new job.

This Jupyter Notebook template is highly adaptable! Apply the same principles to a wide range of binary classification use cases, such as predicting user behavior (will a user choose A or B?), conversion rates (will a person sign up for your product?), or other yes/no scenarios.

This course is designed for individuals with Python and Data Science skills ranging from beginner to advanced. Beginners are welcome – don't hesitate to ask questions! More experienced data scientists will find this a valuable refresher, particularly in the area of model explainability.

Get ready to unlock valuable insights and apply your skills to impactful projects within your organization. Let's dive in and have fun building!