1) Types of Machine Learning:
Types of Machine Learning
There are mainly three types of machine learning: supervised learning, unsupervised learning and reinforcement learning.
A. Supervised Learning:
as the name indicates the presence of a supervisor as a teacher. Basically, supervised learning is a learning in which we teach or train the machine using data which is well labelled that means some data is already tagged with the correct answer.
For instance, suppose you are given a basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this:
· If shape of object is rounded and depression at top having colour Red then it will be labelled as –Apple.
· If shape of object is long curving cylinder having colour Green-Yellow then it will be labelled as –Banana.
Now suppose after training the data, you have given a new separate fruit say Banana from basket and asked to identify it.
Since the machine has already learned the things from previous data and this time have to use it wisely. It will first classify the fruit with its shape and colour and would confirm the fruit name as BANANA and put it in Banana category. Thus, the machine learns the things from training data(basket containing fruits) and then apply the knowledge to test data(new fruit).
Supervised learning classified into two categories of algorithms:
1. Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” or “disease” and “no disease”.
2. Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
| Regression v/s Classification Machine Learning: In a regression problem, we are trying to predict results within a continuous output. In a classification problem, we are trying to predict results in a discrete output (yes/no | 1/0). Example: Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem. We could turn this example into a classification problem by instead making our output about whether the house "sells for more or less than the asking price." Here we are classifying the houses based on price into two discrete categories. |
B. Unsupervised Learning:
unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore machine is restricted to find the hidden structure in unlabelled data by our-self. Here the task of machine is to group unsorted information according to similarities, patterns and differences without any prior training of data.
For instance, suppose it is given an image having both dogs and cats which have not seen ever. Thus, the machine has no idea about the features of dogs and cat so we can’t categorize it in dogs and cats. But it can categorize them according to their similarities, patterns, and differences i.e., we can easily categorize the above picture into two parts. First may contain all pics having dogs in it and second part may contain all pics having cats in it. Here you didn’t learn anything before, means no training data or examples.
Unsupervised learning classified into two categories of algorithms:
1. Clustering: is an important concept when it comes to unsupervised learning. It mainly deals with finding a structure or pattern in a collection of uncategorized data. Clustering algorithms will process your data and find natural clusters (groups) if they exist in the data. You can also modify how many clusters your algorithms should identify. It allows you to adjust the granularity of these groups.
2. Association: This unsupervised technique is about discovering exciting relationships between variables in large databases. For example, people that buy a new home most likely to buy new furniture.
C. Reinforcement Learning:
Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.
A reinforcement learning algorithm, an agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly. The agent learns without intervention from a human by maximizing its reward and minimizing its penalty.
Though both supervised and reinforcement learning use mapping between input and output, unlike supervised learning where feedback provided to the agent is correct set of actions for performing a task, reinforcement learning uses rewards and punishment as signals for positive and negative behaviour.
As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences between data points, in reinforcement learning the goal is to find a suitable action model that would maximize the total cumulative reward of the agent. The figure below represents the basic idea and elements involved in a reinforcement learning model.
RL can guide an agent on how to act in the real world (robot that vacuum your house, robot that learn to walk).
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