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.
This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More
The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More
SYL bank is one of Australia’s largest banks. Currently, the loan applications which come in to their various branches are processed manually. The decision whether to grant a loan or not is subjective and due to a lot of applications coming in, it is getting harder for them to decide the loan grant status. Thus, they want to build an automated machine learning solution which will look at different factors and decide whether to grant loan or not to the respective individual.
In this ML problem, we will building a classification model as we have to predict if an applicant should get a loan or not. We will look at various factors of the applicant like credit score, past history and from those we will try to predict the loan granting status. We will also cleanse the data and fill in the missing values so that our ML model performs as expected. Thus we will be giving out a probability score along with Loan Granted or Loan Refused output from the model.
In this machine learning project , you will predict the total travel time of taxi trips from their initial partial trajectories.
In this data science project, you will be working on building a machine learning model that can identify nerve structures in a data set of ultrasound images of the neck. This will help enhance catheter placement and contribute to a more pain free future.
Build a predictive model to correctly classify products between 9 product categories (fashion, electronics, etc.) using the Otto Group dataset.