Quora is a question-and-answer site where questions are asked, answered, edited and organized by its community of users. Over 100 million people visit Quora every month, so it's no surprise that many people ask similarly worded questions. Multiple questions with the same intent can cause seekers to spend more time finding the best answer to their question, and make writers feel they need to answer multiple versions of the same question.
Quora has publicly released the data set to mitigate the inefficiencies of having duplicate question pages at scale. Which gives us our problem statement : An automated way of detecting if pairs of question text actually correspond to semantically equivalent queries.
Data type: CSV files
Train data: train.csv (id, qid1, qid2, question1, question2, is_duplicate)
Test data : test.csv (id, qid1, qid2, question1, question2)
Total number of records in train data: 404351
Data Size: 130MB
- Validity of this course is 240 days( i.e Starts from the date of your registration to this course)
- Expert Guidance, we will try to answer your queries in atmost 24hours
- 10+ machine learning algorithms will be taught in this course.
- No prerequisites-- we will teach every thing from basics ( we just expect you to know basic programming)
- Python for Data science is part of the course curriculum.
We are building our course content and teaching methodology to cater to the needs to students at various levels of expertise and varying background skills. This course can be taken by anyone with a working knowledge of a modern programming language like C/C++/Java/Python. We expect the average student to spend at least 5 hours a week over a 6 month period amounting to a 145+ hours of effort. More the effort, better the results. Here is a list of customers who would benefit from our course:
- Undergrad (BS/BTech/BE) students in engineering and science.
- Grad(MS/MTech/ME/MCA) students in engineering and science.
- Working professionals: Software engineers, Business analysts, Product managers, Program managers, Managers, Startup teams building ML products/services.