Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
Fraud risk is everywhere, but for companies that advertise online, click fraud can happen at an overwhelming volume, resulting in misleading click data and wasted money. Ad channels can drive up costs by simply clicking on the ad at a large scale. With over 1 billion smart mobile devices in active use every month, China is the largest mobile market in the world and therefore suffers from huge volumes of fraudulent traffic.
In this machine learning project, you will build a machine learning model to determine whether a click is fraud or not.
In this machine learning project, we will predict which coupons a customer will buy.
In this machine learning project, we will use hundreds of anonymized features to predict if customers are satisfied or dissatisfied for one of the biggest banks - Santander
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.