R Tutorial: Machine Learning: What's the challenge?

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-machine-learning-with-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hello! Welcome to the first video of the Introduction to Machine Learning course. My name is Gilles, I'm a content creator at DataCamp and in this course, me and my collegue Vincent will explain some general concepts about machine learning. After this course, you'll be able to tell what machine learning is and what it isn't, how to solve some basic machine learning problems and how to think critically about your data and your results. But first things first: What on earth _is_ Machine Learning? At a very basic level, Machine Learning explores the construction and usage of algorithms that can learn from data. But when does a machine actually learn? We can say that a machine has the ability to learn if it is able to improve its performance in solving certain tasks when it receives more information. This 'experience' typically comes in the form of observations on how particular instances of a problem were solved before. Maybe an example will clarify. A possible task for a computer could be to label squares with a color, based on the square's size and edge . Initially, the computer has no idea how to do this. However, suppose that a number of squares were labelled earlier by humans. For example, a small dotted square was labelled green, a big striped square was said to be yellow, a medium sized square with a normal edge was labeled green as well and lastly a small striped one was labeled yellow. A machine learning algorithm can use these observations, or instances, to do an informed guess about how to label an unseen square. An example could be a medium striped square. The computer can be right or wrong in doing so. This specific example was a classification problem. There are many types of machine learning problems; some are related