What is Machine Learning ?
The field of study that gives computers the ability to learn without being explicitly programmed.
Definition
Here's a definition of what is machine learning that is attributed to Arthur Samuel. He defined machine learning as the field of study that gives computers the ability to learn without being explicitly programmed. Samuel's claim to fame was that back in the 1950s, he wrote a checkers playing program. The amazing thing about this program was that Arthur Samuel himself wasn't a very good checkers player. What he did was he had programmed the computer to play maybe tens of thousands of games against itself. By watching what social support positions tend to lead to wins and what positions tend to lead to losses the checkers plane program learned over time what are good or bad suport positions by trying to get a good and avoid bad positions, this program learned to get better and better at playing checkers because the computer had the patience to play tens of thousands of games against itself. It was able to get so much checkers playing experience that eventually it became a better checkers player than also, Samuel himself.
Two Main Types of Machine Learning
The two main types of machine learning are Supervised Learning and Unsupervised Learning. Of these two, supervised learning is the type of machine learning that is used most in many real-world applications and has seen the most rapid advancements and innovation. By far, the most used types of learning algorithms today are Supervised learning, Unsupervised Learning, and Recommender Systems.
Difference between Supervised and Unsupervised Learning
Supervised Machine learning or more commonly, Supervised Learning (SL), refers to algorithms that learn x to y or input to output mappings. The key characteristic of supervised learning is that you give your learning algorithm examples to learn from. That includes the right answers, whereby right answer, I mean, the correct label y for a given input x, and is by seeing correct pairs of input x and desired output label y that the learning algorithm eventually learns to take just the input alone without the output label and gives a reasonably accurate prediction or guess of the output.
In Unsupervised learning We're given data that isn't associated with any output labels y, say you're given data on patients and their tumor size and the patient's age. But not whether the tumor was benign or malignant, so the dataset looks like this on the right. We're not asked to diagnose whether the tumor is benign or malignant, because we're not given any labels. But in the dataset, instead, our job is to find some structure or some pattern or just find something interesting in the data. This is unsupervised learning , we call it unsupervised because we're not trying to supervise the algorithm.