Machine Learning: Its Pros and Cons
Machine learning is a fundamental type of artificial intelligence. In computer science learning it is called as a sub-set of artificial intelligence. It lays emphasis on the use of data and algorithm to copy the learning ways of humans and with the passage of time it is trying to improve its accuracy. IBM and machine learning has a rich history.
Arthur Samuel has the credit for initiating this term, when he was doing research on a game of checkers. Robert Nealey who is regarded as the checker master, once played the game on an IBM 7094 Computer in the year of 1962. In the field of artificial intelligence this thing is regarded as a major revolutionary segment in the field of AI.
Now-a-days it has emerged as a technology which can transform and reshape industries and doing this in an appropriate way. It has revolutionized the process of the decision making and enhanced the capabilities of a number of applications. This article is exploring key concepts, challenges, applications and potential which is shaping future.
Role of machine learning growing
In the growing field of data science, it is considered as one of the important components. By using statistical methods algorithm are able to give predictions and make classifications. In data mining projects, it is unveiling the key insights and these insights are subsequently used to drive decision making within applications and various classification and businesses with ideally impacting key growth metrics.
In market as the big data is expanding and growing, the demand of the data science will also be increased. It will be the need to help and identify the most relevant business queries and data for answering them.
Summary
Machine learning is a subcategory of artificial intelligence that contains training algorithms to acquire from and make forecasts or decisions founded on data. It empowers computers to advance their presentation on tasks through experience. Machine learning models are skilled, using large sets of data and algorithms that learn from this data to make calculations or detect patterns.
There are several types, including supervised, unsupervised, and reinforcement learning. Applications of machine learning are prevalent, including in parts such as image and speech recognition, medical diagnosis, financial modeling, and self-driving cars. Its rising importance is due to its aptitude to process and evaluate large volumes of data, providing understandings and computerizations that were previously incredible.
What is machine learning and its types
It is a subset of artificial intelligence which gives the capability to machine to learn from the data automatically, using past experiences, improving performances and finally make predictions. Algorithm are provided with date and training and on the basis of it, they perform specific task and build different models.
For solving different problems like classification, regression, forecasting and association, these ML- algorithm help us a lot. On this basis of methods and different ways of learning, it is divided into four major types which are given below:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi- Supervised Machine Learning
- Reinforcement Machine Learning
Supervised Machine Learning
It is clear from its name that this learning is totally based on supervision. It means in this learning machine are trained by using labeled data-set by training machines that predicts output in the form of results. In this learning by input and output, we train the machine and after this we ask the machine to give us prediction, the output by using the test dataset.
The main target of this learning is to chart the input variable with output variable. Risk assessment, fraud detection, spam calculating, are some real-world applications of this learning.
Unsupervised more learning
In this type there is no need of supervision. In this type of learning, machine is skilled by using unable dataset and guesses without any command. Here in this machine learning the machine is trained by using unable dataset and predicts the output without any supervision. Here in this machine learning, the machine is trained with data that is neither labeled nor classified and the machine acts on the data without any kind of supervision.
The main goal of this learning algorithm to divide or categories the unsorted dataset on the basis of differences, similarities and patterns. In this learning instructions are given to machine to search the hidden patterns by using input dataset.
Clustering and association are the subset of it. Network analysis, recommendation system, anomaly detection and singular value decomposition are its applications.
Semi-Supervised Learning
This type of machine learning algorithm lies between unsupervised and supervised learning. So, it is called Semi-supervised learning. It presents the intermediate ground between supervised and unsupervised learning during the training algorithm uses the combination of labeled and unlabeled datasets. This type of learning mostly consists of unlabeled data and works on the data of few labels.
For business purposes, they may have few labels because these labels are very costly. This type of learning is quite different from supervised and unsupervised learning as they have base which is standing on the presence and absence of labels. There are many drawbacks in supervised and unsupervised learning, so this semi supervised learning is introduced.
The main goal of this learning is to make effective use of available data and tells that not only rely on the labeled data as in supervised learning. In the start similar data is collected along with the algorithm with an unsupervised learning and it is supportive to label the unlabeled data into the labeled one.
- In this learning it is easy to comprehend algorithms.
- Its efficiency is highly recommended but not applicable on network level data.
- It solves the shortcomings of supervised and unsupervised algorithm, but its accuracy is very low.
Reinforcement Learning
In this learning feedback-based process learning works in which an agent of artificial intelligence explores its surroundings by different actions and learns from experience and improve its performances.
In this learning, rewards and punishments are given to AI agents on the basis of their actions but the primary of the goal the reinforcement learning agent in this stage is to maximize rewards. In it there is no occurrence of labelled data like in supervised learning.
This process of learning is similar to human beings. On the basis of its working, it is applicable to number of fields such as operation research, information theory and game theory and etc. It has two types
- Positive Reinforcement Learning
- Negative Reinforcement Learning
There are many merits of it as well as its demerits.
- It is helpful in solving real world problems.
- For achieving long term results, it is very supportive.
- The RL algorithm does not give performance to simple problems.
- Huge data and computations are its requirement.
Significance of Machine Learning
It is very significant because it gives view of trends according to the customer’s behaviors and some patterns of operational business, for the development of new products and it is very supportive. Many leading companies of today Like, Facebook, the uber and Google make it a main part of their operations.
It has several applications which can give real business results. It has dramatic potential for the future of any organization. We are using it in health Care, automation, banking and finance, recognition of images and so on.
Pros and cons
Pros
It is very fast and can analyze large data more rapidly than humans.
It can give us highly accurate data
It has reduced our manual work.
It has the potential to develop the innovative products in various fields.
Cons
It is dependent of high- and low-quality data.
It requires intensive resources
It is always in the need of careful monitoring and ethical concerns.
Complexity in developing and understanding models is a major drawback.
Conclusion
In our daily routine, Machine learning is playing a vital role. Various machine learning applications are crucial for living in this technical and digital world. It is rising day by day. It is granting high value prediction and calculation to us. It is very vast field and its importance is not limited. It is very useful everywhere to check and predict different things.
Frequently Asked Questions (FAQs)
