You’ve got the skills, now it’s time to put them all together. Learn what a complete data science project looks like, from data cleaning all the way through to machine learning in our Machine Learning Project course.
The goal of the course is to give you an interactive walk-through of each step in a typical data science project: data cleaning, preparing the features and feature selection, and making predictions. You’ll use loan data from the Lending Club to try to solve a real-world business problem for any lender: predicting whether a loan will be paid off or not.
To predict if a loan will be paid off, you will train, test, and iterate on the machine learning models you’ve learned about in the previous machine learning courses in our data scientist path, including logistic regression models, neural networks, decision trees, and random forests. You will also learn how to choose an appropriate error metric for your model to help you iterate on the model you choose.
At the end of the course, you'll have completed a realistic end-to-end data science project and you will have worked through the entire data science life cycle. This project is a chance for you to combine the skills you have learned so far, and it also serves as a portfolio project that you can showcase to your future employers to demonstrate your skills across a complete data science project.
By the end of this course, you'll be able to:
Walkthrough the Steps of a Machine Learning Project
Machine Learning Project Walkthrough: Data Cleaning
Prepare data on loans for predictive modeling.
Machine Learning Project Walkthrough: Preparing the Features
Learn to work with a more complex API that involves authentication and POST requests.
Machine Learning Project Walkthrough: Making Predictions
Train, test, and iterate on machine learning models.