INTRODUCTION TO MACHINE LEARNING

Hello Learners !! I am Honey Darji . In this blog seeing the title you know what do I discuss in this blog. But you also think what is the new thing on it. Here, I am trying to understand as simple as possible.

First I will give you the example. small babies imitate their parents or anything . They observed all this techniques and style about human and they learned about this. So here yo swap the baby with machine and human with datasets. This concept is machine learning. To make Machine Learn.

So let us learn in technical terms. Machine Learning is the subfield of Artificial Intelligence(AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people.

Suppose that you decide to check out that offer for a vacation . You browse through the travel agency website and search for a hotel. When you look at a specific hotel, just below the hotel description there is a section titled “You might also like these hotels”. This is a common use case of Machine Learning called “Recommendation Engine”. Again, many data points were used to train a model in order to predict what will be the best hotels to show you under that section, based on a lot of information they already know about you.

I give you the example of some machine learning algorithms. All of you watch youtube , In youtube Home page you show some recommendation about videos.

Classification of Machine learning

Machine Learning is majorly divided into three parts using their nature of learning and response of the techniques. So here Main three Classification as follows:

1. Supervised Machine Learning : When an algorithm learns from example data and associated target responses that can consist of numeric values or string labels, such as classes or tags, in order to later predict the correct response when posed with new examples comes under the category of Supervised learning.
2. Unsupervised Machine Learning: Whereas when an algorithm learns from plain examples without any associated response, leaving to the algorithm to determine the data patterns on its own. This type of algorithm tends to restructure the data into something else, such as new features that may represent a class or a new series of un-correlated values.
3. Reinforcement Learning: When you present the algorithm with examples that lack labels, as in unsupervised learning. However, you can accompany an example with positive or negative feedback according to the solution the algorithm proposes comes under the category of Reinforcement learning, which is connected to applications for which the algorithm must make decisions (so the product is prescriptive, not just descriptive, as in unsupervised learning), and the decisions bear consequences. In the human world, it is just like learning by trial and error.
   Errors help you learn because they have a penalty added (cost, loss of time, regret, pain, and so on), teaching you that a certain course of action is less likely to succeed than others. An interesting example of reinforcement learning occurs when computers learn to play video games by themselves.
4. Semi-Supervised Machine Learning: where an incomplete training signal is given: a training set with some of the target outputs missing. There is a special case of this principle known as Transduction where the entire set of problem instances is known at learning time, except that part of the targets are missing.