• Hi, I’m Gerson Ramirez
• I’m interested in Learning new machine learning techniques, improve my programming skills in python, R and more.. Data science inspires me top bridge the relationship between business administration and how to make sense from the multitude of business data to make better decision making and shed light to the meaning behid what the data means.
• I’m currently learning more about deep neural network models including CNN's, RNN's, LSTMS's and how to apply them for NLP practices.
• I’m looking to show you some models i have created as work created for NIU's AI lab NLP project in hopes of attracting paid projects, recruiters, employers, and even school offering scholarships for a PHD in data science. I am interested in full-time, Part-Time, and intership job opportunities.
• 📫 you can reach out to my personal email at: [email protected]

This model contains 1 dense layer and 1 output layer as seeen in the image bellow:

importning the data with Pandas and observe the data. Create x & y training and eval sets. splitting the data 80/20. (note this is usully the recoomended split option but given the small data set we can split it differently maybe 50/50). Please refer to the Jypyter Notebook.

Noise removal which includes all the steps on the image bellow:

-Processed the documents into a unique vocab dictionary with index

• tensored are then created from the indexes+ padding of each sentences as described in the following image:

This project was made possible by the help of Google, coursera, and the Deeplearning.AI NLP Specialization through coursera. If you're interested in learning more about neural networks please visit their specialization courses at coursera.