Here's the latest for Boom Languages, a new language learning app I'm working on.
I just finished getting Version 2.0.0 on all app stores and the web. The main new feature is an "article translation" tool. It works, but as you'll see, it's pretty buggy.
If you'd like to try it out, visit https://boomlanguages.com.
Picking up an old project from earlier this year and making a few additions.
If you'd like to try it out, visit https://boomlanguages.com.
Unfortunately, I may have broken it by changing the server domain name, but stick around until 7/31 - that's when I'm aiming to push out a new (not-broken) update with some of the features you see here, and hopefully more.
In this video, we learn how to create, manage, and remove Python Virtual Environments.
Where do we start with something as complex as scikit-learn? At the beginning, of course!
The Linear Regression module in scikit-learn provides an excellent first step into the world of machine learning. Rather than having to read a 90 page paper, we can instead make use of the knowledge that we gained in our stats class to form a model that will make some predictions for us.
If this sounds like a lot, don鈥檛 worry. We鈥檒l briefly review the math concepts you need to know to fully understand what鈥檚 going on, and also touch on an example with real-world data that will put things in perspective.
Machine learning is fascinating, but it can be overwhelming if you're just getting started. Luckily, scikit-learn is an open-source machine learning library that allows you to jump in and gain hands-on experience immediately, harnessing the power of almost 2,000 contributors without reading a single scientific paper.
If this sounds interesting to you, read on. In this article, we're going to learn what scikit-learn is, how to install it, and how to run it using Python and Jupyter Notebooks.
Everyone has heard of machine learning and artificial intelligence at this point, right?
There is an insane amount of buzz around ML and AI, and for good reason. The techniques available to us today allow for mind-blowing applications that would have seemed like magic just a few years ago.
I've researched all that I can across the web, and I've come up with an analogy: Machine learning is a kind of tool that we can use to make our computers do what we want. Every time you learn a new machine learning technique, you add another tool to you toolbelt.
When you have an assortment of tools at your disposal, you might try your hand at building something rather complex with them, something that requires all of those tools to work together in unison. It's likely that you'd try to build something that exhibits artificial intelligence. In this way, machine learning techniques are the tools, artificial intelligence is the house, and you are the carpenter!
Let's dive in a bit deeper and see if this analogy holds true.
About four months ago, in late November 2020, I finally took action on an ambitious plan that I've had in the back of my head for quite a while. I wanted to build a language-learning app that was just a bit different than the others I've tried.
As with any project, it seemed easy... until I got started!
I created a plan, designed the app, and implemented it as best I could. While it has fewer features than I originally thought I'd be able to get done, the ones that it does have seem to work well so far (in spite of a few bugs people found immediately after it was published).
In this video, we write our first web request.
In this video, we use standard input and output in Python to process data piped to us from other applications.
Some of us grew up clicking around in MS Paint on Windows. Others may enjoyed the luxurious interface afforded by Mac OS. Still others may have been stuck with nothing more than a cell phone, or even just a TI-84 calculator.
Regardless of your humble beginnings, I want to congratulate you on taking things to the next level by jumping headfirst into the world of Linux. Whatever your reason for dipping your toes in these waters, I'm sure you won't regret it! Anyone involved with computers will almost certainly encounter Linux at some point in their career, so now is the time for you to get ahead of things and figure out how to use the dang thing!
All you'll need is a little patience and about 10 minutes to get started! Read on.
Disclaimer: the video is 10 minutes, but the article may be a bit more verbose. :)