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Hello Mi Fans,
Welcome back to yet another Mi Community Tech Class Session. In the previous chapter, we dealt with Fiber Optics and in today's chapter, we will be learning more about Machine Learning and how do they help us in the real world.
What is Machine Learning?
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
Types of machine learning problems
There are various ways to classify machine learning problems. Here, we discuss the most obvious ones.
1. On basis of the nature of the learning “signal” or “feedback” available to a learning system
Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. The training process continues until the model achieves the desired level of accuracy on the training data. Some real-life examples are:
Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. It is used for clustering population in different groups. Unsupervised learning can be a goal in itself (discovering hidden patterns in data).
A simple diagram which clears the concept of supervised and unsupervised learning is shown below:
supervised_learning-.png (8.57 KB, Downloads: 9)
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unsupervised_learning-.png (14.82 KB, Downloads: 10)
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As you can see clearly, the data in supervised learning is labelled, whereas data in unsupervised learning is unlabelled.
2. On the basis of “output” desired from a machine-learned system
An example of classification and regression on two different datasets is shown below:
On the basis of these machine learning tasks/problems, we have a number of algorithms which are used to accomplish these tasks. Some commonly used machine learning algorithms are Linear Regression, Logistic Regression, Decision Tree, SVM(Support vector machines), Naive Bayes, KNN(K nearest neighbours), K-Means, Random Forest, etc
ConclusionWhile many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:
In Case You Missed Previous Chapters:
Chapter 70: All About Fiber Optics
Chapter 69: All About S/PDIF
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