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.
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.
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