Machine Learning Role in Data Science – Why Is It So Important?
If you’ve been upscaling your Data Science knowledge, we bet you’ve come across Machine Learning at some point in your life. But, even if you haven’t, if you’re at least a bit interested in pursuing Data Science as an actual career path–you’ll definitely need to learn all about it.
We know the term sounds abstract right now, and helping you understand is what this article aims to do. We hope that by the end of it, you’ll be versed enough to start learning Machine Learning and dive into the deep waters of Data Science on your own. Now, let’s begin.
What is Machine Learning?
Machine Learning, to put it simply, is a so-called paradigm shift in how we know programming and software. We know that in traditional programming, we write a program, input data, and run that through a computer to get some sort of output that suits our needs. But, this is the part where Machine Learning turns everything we know upside down on its head and involves putting data and output through a computer to get machine-generated programs.
You could look at it as a branch of artificial intelligence concerned with creating algorithms that allow computers to evolve their code without human intervention by giving them some kind of data to start with. If you’ve read our other articles about Data Science, then you definitely know how much automation means for this entire study. And Machine Learning is how model automation is achieved. You’re essentially making it easier for yourself and others by making the computer program itself with the information it’s receiving–and that’s the future everyone’s been dreaming about since the creation of technology itself!
Types of Machine Learning
You need to know about more types of Machine Learning, as every one of them has its purpose in the real world. The three types of Machine Learning are:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Let’s look at what every type of learning does and why you would choose one over the other in different situations when working on real-life projects.
Supervised Learning is a type of Machine Learning that compares its own results with a given training example set. This goes on through the entirety of the process, and every time the Machine Learning process outputs a result, it constantly compares it to the dataset you trained it on. This training set can’t be just about anything–it has to be the best real-world example of what you’re trying to achieve with the algorithm so that the computer can return some type of data that’ll be useful to you or someone else.
SL requires a bit of fiddling to get right, often adjusting parameters and datasets, and changing the algorithm, so it fits your needs just right.
You already know what Supervised Machine Learning is, so think of Unsupervised Learning as the opposite. This type of Machine Learning algorithm doesn’t take in any kind of example dataset, and it doesn’t require you to train it with that dataset before it can make real-life predictions or solve problems.
Unsupervised Learning learns without any kind of help. It’s receiving data and find patterns in the dataset on its own without any sort of human intervention. A typical example of this type of algorithm is “clustering,” which essentially means the algorithm tries to categorize and label given data based on everything it’s seen so far. Of course, it has many more complex uses, but we think this is an excellent starting point for you to finally begin to understand the vast world of Data Science and Machine Learning as a whole.
And, finally, we have Reinforcement Learning, a branch that is concerned with learning the optimal or nearly-optimal path that maximizes the “rewards” function. It means not needing example sets (like Supervised Learning) to determine a way that almost always gives the best result possible and learning from its past mistakes, so it doesn’t repeat them.
Machine Learning in The Real World
We’ve been talking about ideas, concepts, and theory for a while now, and we’re sure you get the image of it so far. But, what about having an idea of where you could use Machine Learning in real life? What kind of projects can you apply it to? And why is it crucial for Data Science? These are all questions that will get answers to as you continue to learn, and soon enough, you’ll be proficient enough not to even need these types of guides anymore.
So, where do we use Machine Learning algorithms? Well, in Data Science, everywhere we can! That seems a bit broad, but that’s the point. Nine times out of ten, you’ll need to automate your Data Science models. This is so they work fast and efficiently–and that means implementing some sort of Machine Learning process in it.
Let’s look at an example of repayment predictions. Sometimes, we need data that tells us how much and how fast people will repay their loans. This information is beneficial to banks and economists, but it’s tough to curate it due to the sheer abstractness of our data. Credit score, gender, occupation, age, income–we have that data, but to our human brains, it’s incomprehensible. That’s where Machine Learning comes in, as we model it after the given attributes and datasets and make it find patterns that we, the dumb humans that we are, can’t notice.
It is done by implementing different formulas and equations and giving the Machine Learning algorithm time and enough data to compare and learn from. With time, the algorithm gets better and better at predicting and starts to provide you with results and answers to questions that are way more accurate than before.