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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 machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.
In this spark project, you will use the real-world production logs from NASA Kennedy Space Center WWW server in Florida to perform scalable log analytics with Apache Spark, Python, and Kafka.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.