Natural Language Processing Notes

Notes from Natural Language Processing Specialization Course 1, Week 3

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Photo by NASA on Unsplash

Continuing on from our Natual Language Processing Notes series, you may have noticed I skipped Week 2. This is not by accident, I realized I have already made considerable notes on Bayes Theorem and Naive Bayes (links below) since this is all that has changed from week 1 to week 2 (the algorithm we use to predict the sentiment of the tweet).

Vector space models are algebraic models that are often used to represent text (although they can represent any object) as a vector of identifiers. …


The Mindset of an Elite Data Scientist

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Photo by Aron Visuals on Unsplash

I intend on speaking with many more lead data scientists, however, if I am totally honest, the reasoning behind why I reached out to them was never to make a blog post.

In fact, the reason I decided to reach out to some of the leaders in our field is that I wanted to gain a better understanding of their mindset. The mindset of a Lead Data Scientist! Quite frankly, I’ve never experienced speaking to one prior to this initiative, let alone working under one — I’ve only ever been the sole Data Scientist (or Machine Learning Engineer).

After speaking to the first two, I left feeling extremely inspired and thinking how life-changing the information and advice I was receiving first hand was. In a blink, I recalled the mission of my blog… To fuel the growth of indispensable Data Scientists, which then resulted in this write up. To further add, my initial instinct was also reiterated to me by one of the Lead Data Scientist, Thom Ives namely, who commissioned me to pass on the message to the community — A modern-day Andrew Carnegie and Napoleon Hill sort of thing, just quite far off being exactly that. …


Leveraging Flask to Serve a Machine Learning Model

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Photo by Lefteris kallergis on Unsplash

For those that aren’t familiar with my writings, in a past post titled “Using Machine Learning To Detect Fraud” we started building the first parts of our Machine Learning package.

Now, the package is complete but what do we do next? How do we integrate this to wherever we need to use it — Package Repository on Github.

I hereby introduce you to the REST API.

Note: This project is highly inspired by the “Deploying Machine Learning Models” course on Udemy, therefore there will be snippets of source code taken from that project.

API is an acronym that is short for Application Programming Interface. Essentially, it is a software mediator that allows for two applications to talk to one another. If you aren’t familiar with API’s then you wouldn’t know you more than likely use one every day. Don’t believe me? When was the last time you sent an instant text message? …

About

Kurtis Pykes

Passionate about fuelling the growth of Indespensible “Data Scientists”. https://www.linkedin.com/in/kurtispykes/

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