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 has been a motivating experience. This has helped me execute Pig Latin and Hive commands to solve data problems. They take special care in regards to answering any questions and doubts I had... Read More
I have had a very positive experience. The platform is very rich in resources, and the expert was thoroughly knowledgeable on the subject matter - real world hands-on experience. I wish I had this... Read More
Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. In the age of artificial intelligence and machine learning the ensemble, methods are becoming new norms, as a stand-alone model won't be sufficient to capture the dynamics of the data variability.
Given a partial trajectory of a taxi, you will be asked to predict its final destination using the taxi trajectory dataset.
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 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.