What Is Machine Learning (Explained in 5 Minutes)

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

Start for Free
The 365 Team 18 Aug 2021 5 min read

Machine Learning is all around us.

Google uses it to provide millions of search results every hour. It helps Facebook guess your next love interest. Even Elon Musk’s Tesla uses it to make self-driving cars. However, if you’re new to the field, Machine Learning can seem daunting.

In this article, we’ll give you an introduction to what Machine Learning actually is.

You can watch the video where we explain the topic in 5 minutes embedded below, or scroll down to keep reading.

How to Build a Machine Learning Model?

Machine Learning (ML) is a method of analyzing data, considered to be a branch of Artificial Intelligence (AI).

During the Machine Learning process, we build predictive models based on computer algorithms containing data. Building a good Machine Learning model can be similar to parenting.

In this analogy, the ML model is the child and the parent is the data scientist working on it. Their main goal is to raise a child capable of solving problems. To become an excellent problem solver, the child has to learn how to deal with the surrounding environment. There are so many unknowns at first, but, over time, their logic will improve. Given enough life experience and useful lessons, the child will become a brilliant problem solver.

This is precisely what we want from an ML model – problem solving skills!

It’s all about learning from experience. Machine Learning does not rely on a pre-written equation. Instead, the algorithm learns from experience in the form of training data. The bigger, higher quality data you have – the stronger the results you obtain from the model.

A child (or adult) may be talented, but perhaps doesn’t have enough experience – especially if they haven’t practiced enough. In such cases, it’s likely that they’ll be outperformed by someone of average talent who continues to learn and work on themselves.

The same goes for Machine Learning models. The more training data you have, the better output you will receive. In most situations, a sophisticated Machine Learning algorithm, trained with a lower amount of data, would most likely perform worse than a fairly simple algorithm with a large amount of training data.

What are The Main Types of Machine Learning?

There are three main types of Machine Learning:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Let's briefly explain each of them.

Supervised Learning

Supervised learning relies on labeled data. Following our previous analogy, the parent is very active in this case, and points out to the child whether a type of behavior is ‘good’ or ‘bad’. In fact, the parent provides plenty of pre-labeled examples. Based on this existing knowledge, the child tries to produce a pattern of behavior that fits the parent’s initial guidelines.

Unsupervised Learning

Unsupervised learning, on the other hand, is an approach used when we don’t have labeled data. Our experiences are unlabeled – they’re not categorized as ‘good’ or ‘bad’. The parent lets the child explore the world on their own. Without initial guidance, they won’t be able to recognize and categorize experiences as ‘good’ or ‘bad’. However, that is not the objective. What the parent aims to accomplish with this kind of technique is that, eventually, the child will distinguish and point out different types of behavior, based on their similarities and differences.

Reinforcement Learning

The third type of Machine Learning is called reinforcement learning. This type is based on feedback. Every time the parent sees a positive behavior from the child, they reward them. Similarly, bad behavior is discouraged with punishment.

As with parenting style, Machine Learning models can be tweaked over time when the data scientist believes that a change of some of the model’s parameters could result in achieving more accurate results. So, very often the art of the data scientist and Machine Learning engineer professions is in the fine-tuning of an already well-performing model. In some cases, a 0.1% improvement in accuracy could be of important significance – especially when the ML model is applied in areas like healthcare, fraud prevention, and self-driving vehicles.

In terms of the complexity of a model that a data scientist can create, we can distinguish between traditional Machine Learning methods and Deep Learning.

What are the Most Popular Traditional Machine Learning Techniques?

Some of the most popular traditional supervised Machine Learning techniques are:

  • Regression
  • Logistic regression
  • Time series
  • Support vector machines
  • Decision trees

These methods allow us to predict a future value or classify our data based on predefined classes.

On the other hand, traditional unsupervised ML techniques, such as K-means clustering, are mainly used for grouping items in the input data into clusters and analyzing patterns in these clusters.

In some instances, data scientists use Principal component analysis (PCA) for the purposes of dimensionality reduction – understanding which are the key variables that make the most significant contribution in a dataset.

What Is Deep Learning?

If Machine Learning is considered a branch of AI, then we can say that Deep Learning is a branch of ML.

The inspiration for Deep Learning (DL) arose from studying how the human brain works. It relies on a structure called a neural network, consisting of multiple layers. In a sense, each of these layers can be considered a classic ML mini-model, and they all learn together.

We can say that a neural network does Deep Learning when it has more than 3 layers. The more layers a neural network has, the more complex it is. And the more capacity for learning it has. In a neural network, each layer’s outputs are inputs for the next layer.

Deep Learning is the best solution for activities like:

  • Image recognition and video recognition
  • Speech classification and speech recognition
  • Natural Language Processing (NLP)

Basically, all the cool AI stuff presented at innovation summits.

Machine Learning vs Deep Learning: How to Choose?

The short answer is: based on the complexity of their data.

The classic approach suffices when we have simpler data, whereas complex data will likely require neural networks. Deep Learning outperforms traditional Machine Learning methods in terms of precision in almost all instances. However, it requires a higher degree of sophistication, is more difficult to interpret, and isn’t as efficient as traditional methods in terms of the time necessary to prepare the model.

The important thing we should remember is that Machine Learning is a tool that can potentially empower people, if applied ethically. It allows us to decrease our workload at scale and is invaluable in situations when we have to deal with a lot of incoming data, and when we have to constantly make a great number of micro decisions.

Machine Learning: Next Steps

Now that you have a basic understanding of what Machine Learning is, you can start learning how to apply it yourself.

Are you ready to dive in?

Try our Machine Learning in Python course for free.

The 365 Team

The 365 Data Science team creates expert publications and learning resources on a wide range of topics, helping aspiring professionals improve their domain knowledge, acquire new skills, and make the first successful steps in their data science and analytics careers.