hey guys. I am starting to learn how machine learning works and what can be done with it.
So I am working on a project- I scrapped a companies vacant jobs from indeed.com and I am trying to map the job titles to the industry it is related to. For example - I scraped ebay's jobs and I am trying to categorize engineer, java engineer and mechanical engineer under engineering. I managed to scrape all the data from indeed and have entered it in a data frame. It is unlabelled - the data frame only consists of the company name and the job title. I cleaned up the data and everything.
What I am having problems with is understanding how deep learning is used in R to map this. I have been reading a lot about how it can be used to map everything but I cannot seem to find a tutorial/ method to actually use deep learning in Rstudio. Can someone help me or direct me to the right place?
I assume I have to use unsupervised learning to label it and create a test and training data subset to do it but I just don't know how to use it. I've been googling about it a lot but no one really gives a tutorial on how to use it but just has code on what they did which isn't helpful as it can be confusing for someone who is learning from scratch.
I had an idea which I don't know if it is helpful or not. Since I did a quick indeed search - for example, I searched engineering and scrapped all of those job titles and put it in a data frame and did the same for customer service, sales, and marketing, business operations, leadership. I thought maybe this data frame could help for deep learning as it is labeled and it could find similarities between job titles and categories and be able to use that information to map it to the master dataframe of Ebay's job titles which are unlabelled if that makes sense. I don't know if this is a good idea or a valid idea so some help would be appreciated there too