Machine Learning In 2021
INTRODUCTION
"If Analytics is the engine, then Data is the 21st century fuel," says Simon Quinton. Businesses would be unable to unearth beneficial insights that may help them streamline their operations if they did not have access to data. Improved customer happiness and personalization will be impossible without customer data. Once upon a time only companies had data, nowadays the data is continuously getting Bigger. So the theory to process such a huge volume of data and turn it into knowledge is the need of the Universe. To solve this problem, Machine Learning came up with the combination of statistics and knowledge representation. Machine learning is the systematic study of algorithms and systems that improve their knowledge or performance with experience.
Artificial Intelligence and Data Science
As per Wikipedia definition AI is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
In simple words AI teaches a Computer (Machine) to learn, think and perform tasks to get the best of best outcome.
AI is implementing a predictive model for forecasting events to come.
On the other hand, Data Science is a systematic method involving pre-processing, analysis, visualization and prediction.
AI uses computer algorithms while Data Science is composed of various statistical techniques. So basically we will understand that AI is an application oriented domain where DS is Analytics oriented, where Analysis is done with the help of various mathematical models. (Statistics, Probability and Linear Algebra, etc).
To better understand, simply get a look at the Venn diagram for AI&DS at the top.
Machine Learning
Machine learning must be one of the fastest growing fields in computer science.
Machine Learning (ML) is considered as a subset of AI. We can even say that ML is an implementation of AI. So whenever we think of AI, we should think of applying ML there. As the name makes it pretty clear, ML is used in situations where we want the machine to learn from the huge amounts of data we give it, and then apply that knowledge on new pieces of data that streams into the system. But how does a machine learn?, that is the question.
There are several methods for teaching a machine to learn. Different methods of machine learning are supervised learning, unsupervised learning, semi-supervised learning, and reinforced machine learning. In some of these methods, a user tells the machine what are the features or independent variables (input) and which is the dependent variable (output). So the machine learns the relationship between the independent and dependent variables present in the data that is provided to the machine. The data which is provided is called the training set. The machine, or the ML model, is then tested on a piece of data that it has never seen before once the learning phase or training is completed. The test dataset is given to this new collection of data. One can divide the existing dataset into training and test datasets in a number of ways. The model will be deployed to a production configuration where it will be used against completely new datasets for issues like prediction and classification after it has matured sufficiently to provide trustworthy and high-accuracy results.
We can take an example of spam filtering mechanisms adapted by mail servers!!!
The incoming mail is filtered out into the SPAM category. The Mail server will decide whether the Mail is SPAM or Not, it will learn from the behavior or history (interest) of recipients. The most common open source computer program used for e-mail spam filtering is SpamAssassin.
You may not be aware of it, but chances are that you are already a regular user of machine learning technology!
In machine learning, there are a variety of algorithms that can be used to solve problems such as prediction, classification, regression, and more. Simple linear regression, polynomial regression, support vector regression, decision tree regression, random forest regression, K-nearest neighbours, and other methods may be heard. These are some of the most often used regression and clustering methods in machine learning. There are plenty more. Even before you train your model, you must take care of a variety of data preparation or pre-processing activities. However, machine learning tools like SciKit Learn have advanced to the point that even an app developer with no training in mathematics or statistics can use them.
Machine learning is a combination of statistics and knowledge representation
Machine learning is the systematic study of algorithms and systems that improve their knowledge or performance with experience
Why Machine Learning?
“Machine learning, big data, and data science skills are the most challenging to recruit for, and can potentially create the greatest disruption if not filled."
Highly salaried jobs
Machine learning engineers are some of the most in-demand workers in artificial intelligence, with an average income of over Rs. 671,548. They are most sought-after by businesses, and are hired for operations immediately if considered necessary. Machine learning engineers come with software skills, natural language handling, statistics, applied math and working knowledge of tools like IntelliJ, Eclipse and more. If you're an AI aspirant, you might want to consider becoming a machine learning engineer for more.
How does it work?
The combination of the best algorithms with the right tools and processes delivers the best Machine Learning model for the computational problem, after all one person’s spam is another person’s ham!
Application of Machine Learning
Machine Learning is a dynamic discipline that has found applications in all sectors, you just name it and find the scope of Machine Learning in it. It may be healthcare services, finance, e-commerce, business and consulting, cyber security, social media, network, entertainment, travel, military, aerospace, biology, home appliances and the list is going on.
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