Our client is an educational website owner who runs a business named trans neuron. He offers skills development courses to working professionals and students.
The business needed us to create a course and job recommendation system for the users. It should use natural language processing to understand users' needs and find the most relevant results.
To create an ML model that offers relevant suggestions based on students' and working professionals' requirements for courses and jobs, respectively.
Our client is a content management services giant who offers services to companies that provide online courses. In this requirement, they wanted to create complete course content along with the final questionnaire to the help of subject matter experts in data collection and exploration. As the test will primarily focus on applicability skills, only 20 out of the 100 questions test the test taker's memory. The rest will focus on the candidate's ability to apply, analyze, and create using their knowledge.
Firstly, we started with creating the project's workflow. In this process, we determined the best technology to use and the step by step of the entire process to ensure appropriate results.
The first step of starting any ML model is collecting relevant data. Here, we started collecting client data that consisted of different variables.
Then, we developed the recommendation model. In this process, we worked on data preprocessing with different variables to find vectors.
Afterward, we used natural language processing (NLP) to convert the text into vectors and these vertors are used to check the similarity between the course and client requirements. In the same manner, we also created the job recommendation system.
In this project, we were hired to create the ultimate recommendation system for the clients using machine learning technology coupled with natural language processing.
To achieve the goal, we first worked on determining the various needs of the ideal users. Then we started creating solutions, from accumulating data to making the final system continuously improve as it receives more data.
The biggest challenge of this project was the amount of scattered data available. Initially, we had to collect various data from the client's websites and other available resources. The data needed to be extended to yield relevant results. But, at the same time handling such a massive amount of data was a challenge on its own.
To create the perfect model, which started with accumulating all the relevant data available online and through the client. Afterward, we worked on handling and preprocessing the data appropriately to get the best model.
In the initial stages, the model did not offer higher accuracy in the appropriate course and job recommendations.
To attain higher recommendation model accuracy, we deployed multiple models and then defined data over time to yield better results
We created the optimized recommendation engine for the business’s client to help them find the most reliable courses and job opportunities.
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