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With this beginner-friendly guide to how machines learn, you'll get a clear understanding of what Machine Learning is and how it works - as well as the main different ways in which it can work.
❇️ Prefer to read rather than watch the video? Check out the blog post here: levity.ai/blog...
Machine learning is a subset of overall artificial intelligence and the general term for when computers learn from data.
Once you get into artificial intelligence and machine learning, there’s no way to avoid three terms:
✨ Supervised learning
✨ Unsupervised learning
✨ Reinforcement learning
There are three main kinds of machine learning. There are key differences between these three kinds of learning, and it's central to understand them and how they work to get a tight grasp of the ins and outs of Machine Learning.
The main differences between these three broad types of machine learning lie in the relationship each of them has between the model's input and output and the method through which they learn. Understanding how these differences affect the algorithm - and therefore the prediction that will be made - is key to maximizing the benefits you could get from Machine Learning.
Main takeaways:
👉 Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.
👉 In supervised learning, we already know that there is a relationship between input and output and, therefore, feed the algorithm with labeled data. The goal is to make predictions when given new input data.
👉 In unsupervised learning, we don't necessarily know the relationships between variables in the dataset. The goal of the algorithm is to gain knowledge and find structure.
👉 In reinforcement learning, the algorithm learns through trial-and-error in an interactive environment using feedback from its own actions. The goal of maximizing long-term rewards drives the decision-making process.
Let us know if you have any questions after watching this informative, introductory video on how machine learning works!
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