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3 Different Types Of Machine Learning
Machine Learning 5 MIN READ June 8, 2021
As we all know, in the recent scenario, machine learning is excelling in every field with its commendable applications. So, it becomes absolutely important for us to know about it and be updated with this current technology that has great future scope as well. In this blog, we’ll know about this technology and understand the Types of Machine Learning. Let’s start from the basics to get there!
What is Machine Learning?
Machine learning is an extremely futuristic subfield that falls under artificial intelligence, where computers can ‘learn’ through statistics, data, and trial & error so as to optimize processes and bring relevance at faster rates. The practical answer to ‘What is machine learning?’ is that systems no longer need to rely on millions of code lines to perform the calculations. ML provides systems the ability to grow human-resembling learning capabilities, which allow them to solve a number of the world’s complex problems, ranging from climate change to cancer research.
Now, let’s know about its types-
Three Types of Machine Learning-
In this part, we’ll know about the three types of machine learning namely, ‘supervised learning’, ‘unsupervised learning’, and ‘semi-supervised & reinforcement learning’.
1. Supervised Learning-
In supervised learning, we basically train an algorithm & in the end, pick a model that predicts well-defined output following the input data.
Supervised learning techniques get used to the model so as to reproduce outputs that are known from a particular training set (for example, recognizing bike types on photos). In the starting, the computer receives both input and output data. Its mission is to create suitable rules that map that input to its output. This training process has to continue until the performance level becomes high enough. After the training, the computer is assigned an output object that it hasn’t seen during its training phase. In many cases, this training process is very fast & accurate.
Supervised Learning is further divided into two techniques: one is Regression and the other one is Classification. Classification technique separates the data, while Regression technique fits the data.
2. Unsupervised Learning-
In this type of ML, we don’t have any outcome variable to predict. The system is trained with the data, which is unlabeled. Unsupervised learning technique aims to find out hidden structures, like revealing groups of photos involving similar cars, but it is a bit difficult technique to implement & therefore, isn’t used as extensively as supervised one.
Unsupervised learning techniques can be used as an introductory step prior to applying the supervised ones. The inside structure of the data might give information on how to reproduce better outputs.
In unsupervised learning techniques, we’ve clustering & dimensionality reduction. The clustering technique is used to uncover similarities & differences. It groups similar data. The dimensionality reduction technique is used to have a better (least complex) depiction of the data. After the application of the Dimensionality reduction technique, the data set has a reduced quantity of redundant information, and the important parts get emphasized.
3. Semi-Supervised Learning & Reinforcement Learning-
In the last two types of ML techniques, either labels or no label is present for all of the observations. But sometimes, we require something between the above two. In such kinds of situations, we rather use the technique of Semi-Supervised Learning that refers to a process in which a lot of output values (which we want to get predicted) are absent. It needs to apply both of the supervised & unsupervised processes to get useful results.
In some cases, the required output value isn’t known clearly, but the systems provide feedback on the given output. The learning that is based on such feedback is known as Reinforcement Learning. For example, this learning is used to train gaming AI in video games like NERO.
Conclusion-
Machine Learning can recognize patterns that we’re unable to find or observe in a huge amount of data. There are different types of machine learning algorithms that are appropriate for different kinds of situations, such as supervised learning, unsupervised learning as well as semi-supervised & reinforcement learning, which lie somewhere between the previous two. Altogether, they help us to solve many problems & make innovative discoveries. If you need the best AI & ML solutions, contact Ksolves India Limited at your comfort.
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