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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
I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... Read More
Customer churn refers to the situation when a customer ends their relationship with a company, and its a costly problem. Customers are the fuel that powers a business.
Loss of customers impacts sales. Further, it’s much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn.The good news is that machine learning can help. For many businesses that offer subscription-based services, its critical to both predict customer churn and explain what features relate to customer churn.
Older techniques such as logistic regression can be less accurate than newer techniques such as deep learning, which is why we are going to show you how to model an ANN in R with the keras package.
In this project, we are going to predict item-level sales data using different forecasting techniques.
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.