In this section, I will be introduced to subsets of Machine Learning and examine each technique.
First of all, let’s look at machine learning. The main question is What is Machine Learning?
Machine learning is the science and art of programming computers so they can learn from data without being explicitly programmed. For instance, our computer can learn to predict whether an email is a spam or not spam given its content and sender. Another example is our computer can learn to cluster, say, Wikipedia entries into different categories based on the words they contain. Notice that, in the first example we are trying to predict a particular class label, that is spam or not spam. In the second example, there is no such label. If there are labels, we call it supervised learning when there are no labels, we call it unsupervised learning.
There are so many different types of Machine Learning systems that whether or not they are trained with human supervision like supervised, unsupervised, and reinforcement learning. Let’s look at each of these a bit more closely.
Unsupervised Learning the training data is unlabeled and the system tries to learn without a teacher.
Supervised learning is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. The goal of supervised learning is frequently to either automate a time consuming or expensive manual task such as doctor’s diagnosis or to make predictions about the future.
Reinforcement Learning is about taking suitable action to maximize reward in a particular situation.
The problem is as follows: we have an agent and a reward, with many hurdles in between. The agent is supposed to find the best possible path to reach the reward. The image shows the robot, diamond, and fire. The goal of the robot is to get the reward that is the diamond and avoid the hurdles that are fired. The robot learns by trying all the possible paths and then choosing the path which gives him the reward with the least hurdles. Each right step will give the robot a reward. and each wrong step will subtract the reward of the robot. The total reward will be calculated when it reaches the final reward that is the diamond.
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