Machine Learning is the Science that allows software applications to work on their own without being explicitly programmed thus enabling them to become accurate at predicting results. It is a way of programming the machines by using the historical data and based on that asking them to predict the new output values. It is the study of computer algorithms that automates the analytical model building by improving automatically through experience and by making use of historical data.
Machine Learning is perhaps the most interesting technology that has been developed which gives computers the ability to learn. Machine Learning is a process that starts with feeding good quality historical data and then based on that data training the machines to predict outcomes based on similar kinds of data. For training the machines, different machine learning models using different kinds of data and algorithms are built. The algorithms are chosen based on what kind of task is to be performed and what sort of data is to be made to work.
What is Machine Learning?
A branch of Artificial Intelligence and computer science that focuses on providing computer systems with the ability to automate themselves and learn and improve from their own experience without having the need to program it explicitly is what is known as Machine Learning. It emphasizes the development of computer systems that once fed with historical data can make future decisions without any external programming requirement. So, it is simply that the computer systems can access the data and use it for learning on their own. The procedure of learning begins with feeding the system with some external data such as direct experience, instructions or observations to look for some pattern in that data and based on the pattern observed using it for making better decisions in the future. So, the main aim of Machine Learning is to permit computers to learn by themselves without any human intervention and perform actions accordingly.
Importance of Machine Learning
Machine Learning is one of the most important subareas of Artificial Intelligence. The concept of Machine Learning of making the computers get into an autonomous learning mode and learn, grow, transform and develop by themselves without being explicitly programmed is what makes it a completely unique concept. Machine Learning techniques have replaced the traditional statistical techniques of data extraction and interpretation works by making use of automated sets of generic methods. As of now, Machine Learning is used in several fields such as in self-driven cars, online recommendation engines, offering suggestions, cyber fraud detection and many more.
In all Machine Learning has made a market for itself and is continuously evolving. And with this continuous evolution comes the factor of rise in demand and importance. So basically, Machine Learning as a technology helps in analyzing bigger amounts of data thus easing the work of the data scientists in the automation process. This is the most prominent reason Machine Learning gained a lot of recognition, importance and prominence.
Methods of Machine Learning
In Machine Learning, there are few algorithms based on which the machine is trained in such a way that it can perform future actions. Classical Machine Learning categorizes the algorithms into four basic approaches for more accurate prediction results. The type of algorithm that data scientists use depends on the type of data that they want to predict. So, the algorithm types are as follows:
- Supervised Machine Learning algorithm:
In Supervised Machine Learning algorithms, historical data is used to predict the results. Here the data that was used in the past is fed into the system and the system analyses that known dataset based on which the learning algorithm produces an inferred function for making predictions of the output values. The learning algorithm also compares the outputs with the intended output and discovers errors to modify the model accordingly. The system once sufficiently trained can provide targets for any input. - Unsupervised Machine Learning algorithm:
This type of algorithm is used to train models when the available data is neither classified nor labelled. So here the models are trained in such a way that they can infer a function and then describe a hidden pattern or structure from that unlabelled data. In this type, the system isn’t able to figure out the proper output but by exploring the data is capable of drawing inferences to describe the hidden structures in the datasets. - Semi-supervised Machine Learning algorithm:
The semi-supervised Machine Learning algorithm is a blend of both the supervised and unsupervised machine learning algorithms. Here, a small amount of labelled data and a large amount of unlabelled data are made use of for making the predictions. It is an algorithm that helps the system improve the learning accuracy as the acquired labelled data requires skilled resources for training the system whereas the unlabelled data doesn’t require any additional resources. So, a mixture of the two results in the system itself improving the learning accuracy. - Reinforcement Machine Learning algorithm:
It is a slightly different learning algorithm wherein the system interacts with the environment by producing certain actions and based on that discovers errors or rewards. This procedure allows the machines and the data scientists to determine the ideal behaviour automatically within a specific context to improve performance and efficiency. Simple reward feedback is required to choose which action is best and this is what is termed as reinforcement signal. Trial and error search and the delayed reward are some of the most popular characteristics of reinforcement learning.
Why should you learn Machine Learning?
Machine Learning today has made its influence fall in almost all industries. Today has got all the attention because it can automate most of the tasks out of which the ability to imitate humans and perform accordingly is considered to be the best. Replicating human intelligence to machines is something that can be achieved only with Machine Learning. Image recognition, text generation, bio-metrics detection, flaw detection and many more cases are finding the applications of Machine Learning in the real world.
Businesses now can automate most of their processes because of this technology. It also helps in automating and creating quick models for data analysis which can then process and analyze large sums of data to deliver accurate results. An additional advantage that Machine Learning provide is that these models created using Machine Learning are precise, scalable and function with very little turnaround time. Various industries are now making use of this technology as they are mostly dependent on vast quantities of data for their industrial operations and for intelligent decision-making. So, Machine Learning provides the ease of performing all the tasks by building such precise learning models that their accuracy is way far better than what used to be earlier. Businesses can now easily avoid unknown risks and leverage profitable opportunities. So, all this creates an increased scope for Machine Learning experts to showcase their talent as professionals.
Working on Machine Learning
For any Machine Learning model to work, some steps need to be followed which are Gathering Data, Preparing the Data, Choosing a model, Training, Evaluation, Hyper-parameter Training and Prediction.
The model is the system which when trained is asked to make predictions. The parameters are the factors based on which the model can make the predictions. The programmer makes certain adjustments such that the model aligns itself with the parameters in such a way that the predictions made by the model match the actual result. Here the working of a Supervised Learning Machine Learning model is elaborated.
- So, the first step in making a model work is to collect some sample data.
- The next step is to classify the sample data based on certain parameters and then describe each of the classified data.
- This data is then fed into the system and the model is trained based on that sample dataset. There may be a few parameters or maybe even a thousand parameters on which the Machine Learning model is to be trained.
- During this process, many errors may occur in the dataset so they are removed from the training set and then the anomaly-free data is used for training the model.
- This is then followed by testing the dataset. A testing set is basically some other dataset on which the trained model is made to work and if the result matches the expected output, then the model is declared as successfully trained and tested model and can now be made to predict results for similar databases.
Machine Learning algorithm is used for building intelligent systems that can learn from their past experiences and based on that modify themselves to deliver accurate results. Many industries are thus making way toward Machine Learning solutions to boost their profits and also to create newer and better products and services for customers. So, all this gradually is resulting in the creation of more jobs in the Machine Learning field. And as the demand is increasing it results in more pay scale too.
