Over the last few months as I’ve approached natural language processing (NLP) I ploughed through several Udemy courses, covering software and mathematics, then set up a project and a preliminary portfolio.

Having a specific target for education within the computer sciences and STEM topics is both relieving and overwhelming.

## A Short Affair with MATLAB, then on to Jupyter

This “MATLAB On Ramp” course gave me a good idea of how the popular MATLAB software functions. This software is dedicated to science topics and is useful for Linear Algebra.

The time I spent on the course, however, was only partial useful, as I eventually let go of MATLAB in favor of Jupyter software, instead.

I like how Jupyter allows for the user to use a full programming language, Python. The community of Python is large and enthusiastic, therefore by using Jupyter, and thus Python, I find myself progressing in more topics than one.

### A Brief Taste of Linear Algebra

The “Complete Linear Algebra” course was interesting for the first third, but I found that the content moved too swiftly through the concepts for the course to be useful. I got the gist of Linear Algebra, and that’ll have to be enough for now.

### The Complete Machine Learning Bootcamp

This Udemy course by App Brewery, “The Complete Machine Learning Bootcamp,” really hit the sweet spot.

At my current point in my own learning curve of NLP, I have so many disparate courses and topics that I’ve studied over the years, and pulling these topics together into working memory is a challenge.

I’ve studied computer science, business statistics, data science, distributed systems, networking, digital and decentralized team dynamics, creative and technical writing, presentation, and so many others.

*(As an aside, I think it relevant to mention how NLP fits into many of the other studies I’ve approached. NLP is a niche field (growing rapidly) that is a subset of computer science and mathematics. To become proficient in NLP, a student must first gain confidence with data science, programming and machine learning, and then linguistics.)*

The course above starts at a point that takes advantage of many of the other courses I’ve taken, and pulls many of the key topics therein into an approachable pathway into machine learning.

Typically, with Udemy courses I only complete about a two thirds of the video content, depending on how committed I feel once I have the majority of the study out of the way.

The machine learning bootcamp course, however, is one that I will probably finish at least 90%, as the content is strong and relevant.

### Using Vuepress to Set Up My Preliminary Portfolio

Over the last few years I have used markdown formatting and Vuepress extensively in my professional work as a technical communicator and documentation writer.

Therefore, I feel it natural to activate Vuepress for the portfolio section of my website.

The portfolio itself is up and running, but I will wait to connect it to this website and blog until the presentation is cleaner.

## Working with a Mentor

I’ve taken so many university courses over the last several years; alas, I am still years away from a new official degree. The road is long… and expensive. I’m not giving up, but I need to find some shortcuts to a more specific data-science or otherwise NLP-related income source.

While I continue working towards that goal, I hope to find a way to get paid to take classes, instead of the other way around. So, I’m looking for a slight shortcut or two, and to that end I hired a mentor from mentorcruise.com.

So far, our time has been productive. My mentor is helping me stay focused on natural language processing, with the intent of having a few high quality portfolio pieces to show by next summer.

### First Attempt at a Data-Science Portfolio Piece

Just like the heading says, I did make one first attempt at a STEM topic portfolio piece.

The topic was to perform price predictions for housing data. I’ve done similar studies like this before, but only as a part of following along with a teacher or a video tutorial.

This time, I tried to take some raw data from kaggle.com and do a study all on my own.

Thus far, I’ve spent perhaps ten or fifteen hours on the project, and I’m not making much headway. My mind is stretching and I’m learning new things, but the project is revealing to me what I truly know and what I do not, and what I do not know in NLP currently vastly outweighs everything else.

But, with continued practice and work, I hope to change this over the next few months.

Until we meet again, dear reader, thank you for following.