Let’s Talk about Machine Learning Classification
Online machine learning courses are having great momentum these days. An important reason that explains this demand is the revolutionizing potential of machine learning. Machines are smarter and continue to make life and business easier for us. This article aims to give you a fair insight on the crux and classification of machine learning.
According to Ray Kurzweil, an American inventor, and futurist “By the time we get to the 2040s, we’ll be able to multiply human intelligence a billion-fold. That will be a profound change that’s singular in nature. Computers are going to keep getting smaller and smaller. Ultimately, they will go inside our bodies and brains and make us healthier, make us smarter.”
What exactly is machine learning?
Simply put, machine learning is a method by which data analysis and automatic data modeling are done by machines with barely any human intervention. Machine learning is used in everyday life — for instance identifying chronic cancer cells in your body during a medical diagnosis. This can be done by applying machine learning in the department of oncology. Machine learning can be applied in diverse scenarios like — predictive analysis of fraudulent transactions on your credit card.
Classifications of machine learning –
Machine learning is a subset of artificial intelligence. Certain types of machine learning algorithms are used for particular use-cases. Leaving these specific cases, in general, machine learning algorithm classifications are trained through three different methods. The following are the methods or types of machine learning that are broadly used –
- Supervised learning –
Supervised learning work with labeled data. Basically, there are two types of data — labeled and unlabeled. As the name suggests, the former has some specific label assigned to it while the latter consists of data from all over the place, in all sorts. Labeling of data helps identify data characteristics and classifications.
Let us see how it works — In the training phase of supervised learning, data of known input values (say labeled input data sets) are utilized, and input-output mapping is done. Both input and output are furnished to the system in this phase. The only work that the system is supposed to do is mapping an input to its relative output by creating specific rules. Later after training, the system is expected to have the ability to map output objects on its own even when given new input data that is not familiar to it.
Supervised learning is task-driven. This means it aims to predict the next value. The two important techniques coming under supervised learning are regression and classification.
- While fitting of data is given focus under regression, data separation is emphasized in classification.
- As the name suggests, data gets separated into classes in the latter technique. Regression in general terms deals with the prediction of output values.
Classification algorithms in machine learning
On the basis of existing data, new observations are categorized using a supervised learning technique, which is the classification algorithm. Learning is primarily done here with a given data-set and information is further classified into groups, under this technique.
ML Classification can either fit into the category of linear model or a nonlinear model. Logistic Regression, Naïve Bayes, K-Nearest Neighbours and decision trees are some important types of classification algorithms. While logistic regression comes under a linear model, the rest of others come under nonlinear models.
2. Unsupervised learning –
Unsupervised learning work with unlabeled data. In the absence of labeled data, predicting output is a little difficult under this method. Most widely, unsupervised learning is used as a preparatory preliminary procedure before going on with the supervised learning approach.
Clustering of unlabeled data sets is done under unsupervised learning using machine learning algorithms. Unsupervised learning is task-driven. Meaning, it focuses on identifying clusters.The important unsupervised learning techniques include dimensionality reduction and clustering.
- In simpler terms, dimensionality reduction means eliminating redundant and unwanted information and keeping the significant ones.
- Clustering means grouping things together after the identification of data based on how similar or dissimilar they are.
Given a condition where many output values are missing, another technique called semi-supervised learning is also devised. This combines both supervised and unsupervised learning.
3. Reinforcement learning -
Under this technique, learning is done through trial and error. Machine learning models are trained to generate decisions in different given scenarios.The desired output is reinforced and the undesired ones punished. Even those complex problems that cannot be solved using conventional techniques can be solved using reinforcement learning.
Given a particular situation, reinforcement learning aims to generate optimal learning solutions and maximize rewards. The application of reinforcement learning is extensive. For example, in finance — Prediction of stock prices and the choice of holding or selling stock by a reinforcement learning agent is expected to deliver favorable outcomes.
Why is machine learning algorithm important?
A user feeding a computer with inputs so that it generates recommendations that are data-driven — this is basically done under machine learning. Machine learning algorithms like — Linear regression and classification and regression trees, help deliver better decision outputs.
Even post-model deployment, if improvements are required, machine learning algorithms are extensively used. In real-time there are many applications of ML algorithms. You get to learn the core concepts relating to machine learning algorithms and their applications through online programs offered by Skillslash.
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