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 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
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
E-Commerce applications provide an added advantage to customers to buy a product with added suggestions in the form of reviews. Obviously, reviews are useful and impactful for customers who are going to buy the products. But these enormous amounts of reviews also create problems for customers as they are not able to segregate useful ones. Regardless, these immense proportions of reviews make an issue for customers as it becomes very difficult to filter informative reviews. This proportional issue has been attempted in this project. The approach that we discuss in detail later ranks reviews based on their relevance with the product and rank down irrelevant reviews.
This work has been done in four phases- data preprocessing/filtering (which includes Language Detection, Gibberish Detection, Profanity Detection), feature extraction, pairwise review ranking, and classification. The outcome will be a list of reviews for a particular product ranking on the basis of relevance using a pairwise ranking approach.
Forecast the business for the upcoming years by Exploring Hidden Trends, Calculating Machine Productivity , Extrapolation and Assumptions and Summarizing Answers through Visualizations.
Deep Learning Project using Keras Deep Learning Library to predict the effect of Genetic Variants to enable personalized Medicine.
In this project, we will use traditional time series forecasting methods as well as modern deep learning methods for time series forecasting.