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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 worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More
Recruiters and HR teams in companies have a tough time scanning thousands of qualified resumes. Either they need many people to do this or they miss out on qualified candidates. This is a waste of time, money and productivity for the company.
To solve this, our resume parser application can take in millions of resumes, parse the needed fields and categorise them. This resume parser uses the popular python library - Spacy for OCR and text classifications. First we train our model with these fields, then the application can pick out the values of these fields from new resumes being input.
The dataset of resumes has the following fields:
In this project, we are going to talk about insurance forecast by using regression techniques.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this data science project, you will be working on building a machine learning model that can identify nerve structures in a data set of ultrasound images of the neck. This will help enhance catheter placement and contribute to a more pain free future.