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.
Recently I became interested in Hadoop as I think its a great platform for storing and analyzing large structured and unstructured data sets. The experts did a great job not only explaining the... Read More
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
Another year, another chance to predict the upsets, call the probabilities and put your bracketology skills to the leaderboard test. In this keras deep learning project, we will once again attempt to predict the outcomes of this year's US men's college basketball tournament. But unlike most deep learning projects, we will pick the winners and losers using a combination of rich historical data and computing power, while the ground truth unfolds on national television.
If you are unfamiliar with the format and intricacies of the NCAA tournament, we encourage reading the wikipedia page before diving into the data. The data description and schema may seem daunting at first but is not as complicated as it appears.
As a reminder, you are encouraged to incorporate your own sources of data. We have provided team-level historical data to jump-start the modeling process, but there is also player-level and game-level data that may be useful.
We extend our gratitude to Kenneth Massey for providing much of the historical data.
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset using Keras in Python.
In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.