If you want to work in AI, you need to know the ins and outs of machine learning. This machine learning for dummies guide will teach you what you need to know.
Machine learning and A.I., in general, are super hot fields at the best tech companies.
They're hiring, but you need to know your stuff to get one of these great jobs. Not sure where to start? A guide to machine learning for dummies should help you learn the basics.
Keep reading for your quick guide to landing a job in this hot field.
If you want to understand how machine learning works, you need to understand how they're different from us. Machines can do things fast. Machine learning is about applying statistical methods, and doing it over and over again, really fast.
Here's an example: Say you have a giant pile of data, like all the credit card receipts in a big box retail store. Imagine you have the purchasing records for millions of people over a period of time.
Now the question comes up, should you spend a million dollars to build a factory that produces pasta noodles? Hmm ... that's a good question!
With machine learning, you can analyze the huge pile of data in ways that aren't automatically apparent.
Are tomato prices going up? Of the people who eat pasta, are they spending more money in general, or are they spending less money in general? How about the people who don't want to eat carbs at all, what are their spending habits?
These types of questions can provide insights that aren't easily seen. Machines can handle huge piles of data, and approach problems from many different directions. Complex problems can be solved this way.
If you want to get a job in the machine learning world, you need to be able to impress the hiring manager. You need a solid foundation in the four-way axis that makes up machine learning as a career.
Software engineering, or computer programming, is the foundation of machine learning basics. You need to know how to program computers.
In order to be able to teach a computer how to learn, you have to understand the theory of mathematics and statistics that underline the model you are trying to train.
Speaking of models, in machine learning you put forth a model of how the world works, and test that against actual data. If your model resembles the real world, you might be on to something! If your model doesn't resemble the real world, you might need a little more work before you pitch it to investors.
Now that you're a programmer, you understand the math, and you can create models, you have to know how to apply all this stuff to the real world. A software engineer has to take all the hypothetical stuff and make it real in the world.
While this guide to machine learning for dummies is a great start, succeeding in this position will take serious dedicaiton. You're going to have to demonstrate experience and aptitude to get a job in the machine learning space. This is one career path where you can't fake it, so you need to show the skills if you want to succeed.
Check out our blog for more useful tips on getting ahead in the tech industry, and learn how you can set your career on the path to success!