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 is one of the best of investments you can make with regards to career progression and growth in technological knowledge. I was pointed in this direction by a mentor in the IT world who I highly... Read More
I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More
About the Carvana Image Masking Challenge Neural Network Project:
As with any big purchase, full information and transparency are key. While most everyone describes buying a used car is frustrating, it’s just as annoying to sell one, especially online. Shoppers want to know everything about the car but they must rely on often blurry pictures and little information, keeping used car sales a largely inefficient, local industry.
Carvana, a successful online used car startup, has seen an opportunity to build long term trust with consumers and streamline the online buying process.
An interesting part of their innovation is a custom rotating photo studio that automatically captures and processes 16 standard images of each vehicle in their inventory. While Carvana takes high-quality photos, bright reflections, and cars with similar colors as the background cause automation errors, which requires a skilled photo editor to change.
To develop an algorithm that automatically removes the photo studio background. This will allow
Carvana to superimpose cars on a variety of backgrounds. You’ll be analyzing a dataset of photos,
covering different vehicles with a wide variety of year, make, and model combinations.
In this data science project, we are going to work on video recognization data and a robust level of image recognization MNIST data.
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.
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.