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.
I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... 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.
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.
In this data science project, we will predict the number of inquiries a new listing receives based on the listing's creation date and other features.
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.