Welcome! If you’re new around here, you may be wondering what this site is all about.
Elle Knows Machines is your place for everything related to artificial intelligence, machine learning, and data science.
It’s a resource for anyone looking to blaze their own path on the cutting edge of cool.
Basically, this website is here to help you unleash your inner AI superpowers and this guide will get you started a$ap. So what are you waiting for??
🤖 Wondering what all the hype is about?
Here are the must-read posts that will get you up to speed so you can show off just how smart you are.
Seriously, if you do nothing else while you’re here, read these posts.
They’ll get you started, make you feel even smarter, and get you fired up to become part of one of the most important movements in technological history.
If you’re a total newcomer to the world of AI start with the post What is Artificial Intelligence?
Looking to go a bit deeper? Check out these starter guides on machine learning and natural language processing.
Wondering why any of this matters? Good question. Read more about why you should join the AI revolution.
🔨 Interested in doing it yourself?
If you want to learn how to work with these algorithms yourself then you’ve made a great choice, my friend. We’re talking job security, big paychecks, and brainy OG creds #foreal.
Here are my top tips on how to get started with artificial intelligence right now 👇
No matter which path you decide to focus on you’re going to need a computer. For beginners looking to get started right away, a basic computer that you can install software on will most likely be sufficient, although the faster the better.
If you want to try your hand at more complicated algorithms like neural nets or generative adversarial networks you’re going to need a computer with higher processing power. Ideally, this means a computer that has a minimum of 8gb of RAM, a powerful graphics card (like the ones in NVIDIA’s GEforce 10 series), at least an Intel i5 processor, and 1tb of hard drive space.
Mice, keyboards, headphones, and external hard drives are optional but bonus points if they look good 💍
When it comes to AI and data science programming languages there are two main options – Python or R.
Unless you feel strongly otherwise, I recommend starting with Python.
It’s widely used in both machine learning and web development so you’re basically doubling your investment if you master it. Plus, it’s widely acknowledged that once you learn one language it’s easier to pick up a second (or third or fourth..) so you can always learn R later on.
If you’re new to Python, check out this guide, then get this book and play this game.
As with any project, you’re going to want to set up your workspace before you get started. For machine learning, data science, or AI this means first installing Anaconda so you have Python on your computer. Anaconda comes preloaded with some of the most popular machine learning libraries which will help you get started right away.
It’s likely, however, that you’re going to want and/or need additional libraries. It’s a good idea to install them all at once. If you don’t you might find yourself in a situation where nothing is working and you’ll want to throw your computer out the window. True story.
Before you get into the flashy stuff like making your computer write Harry Potter fanfiction or generating fake celebrity images, you’re going to need to clean up your data so that your computer can do its thang.
We’re at a time in history where more data is being produced each year than all of the data produced by humans – in all of history. So yeah, data. It’s pretty big. That’s good news for anyone looking for data to use in their own projects. If you’re looking for datasets to feed your machines, this is a good place to start.
Check out these key algorithms when you’re ready to get your machine humming: linear regression, logistic regression, neural networks, svm, pca, k-means clustering, and recommender systems. And here’s where you can find all of the tutorials that walk through how to use these algorithms IRL. If you’re unsure where to start I definitely recommend working through the above list in order, starting with linear regression first ☝️
If you’re looking to take your skills to the next step read about how to make your own machine learning curriculum. Luckily, there are some top-notch resources available that will help you take your game to the next level.
Learning some (just enough!) statistics, linear algebra, and calculus won’t hurt either.
I know math has a bad rep but contrary to what your high school math teacher might have led you to believe – it doesn’t actually have to be painful.
Math is the language of the universe which means it can be used to basically explain everything 🤯 Plus, it’s what all of the cool topics like machine learning, AI, robotics are built on.
I think it’s time we #makemathcoolagain.
If you’ve gotten this far then you’ve earned some serious street cred. It’s time to show off. A portfolio filled with all of your swish projects is a great way to prove to potential employers, friends, and strangers that you’re rocking data-fueled superpowers.
Building your own portfolio website will take you to the next level 💯
👋 Want to say hi?
If you have questions, comments, or just want to say hi drop me an email at hello@elleknowsmachines.com. For interview requests, collaboration ideas, or other opportunities email collab@elleknowsmachines.com.