The process of making computers and digital gadgets learn and perform activities the way humans do, Machine Learning focuses on data-driven learning based on real interactions.
A branch of the much popular concept of Artificial Intelligence (AI), Machine Learning (ML) converts data into food for learning for machines and applications that enable computers to carry out humanly functions. Over time, this data improves the way machines work and refines their accuracy throughout the process.
With humongous data being fed to such machines, ML uses numerous algorithms to enable computerized machines to detect patterns and discover common grounds. With AI becoming more and more necessary in the digital realm, machine learning algorithms have a big role to play in big data. While data is the food for machines, ML is the soul of computers wherein data is segregated and interpreted accordingly.
From numbers to names, words to vast information, ML percolates through the enormous data that it is fed with, and looks through it for patterns and grounds that serve mankind through various applications.
“Machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.” Machine Learning
Machine Learning Tutorial involves the working of ML under the lens of Artificial Intelligence. With so many applications of Machine Learning surrounding us in our day-to-day lives, the working of ML can be understood through a range of steps that we will be discussing in this segment. Let’s get started.
The key to Machine Learning is data. With data everywhere around us, machines have eventually become the producers of information. Beginning with past data records, machines go through the data and find relevant patterns that help them establish future outputs. Even though they are mere predictions, ML facilitates accuracy over time as more and more data is ingested by computers.
With more than a hundred ML algorithms being published every single day, Machine Learning Tutorial is based on various learning styles that help computers interpret data and extract the meaning out of it. There are 3 different types of learning in ML or methods of Machine Learning that motivate computers to interpret data in a particular manner. They are as follows:-
Types of Learning Styles in ML
In the first type of learning style in ML, data is labeled and computers are tagged with the patterns they are required to identify. This means that computers are supervised in the way they interpret the data and are fed with the grounds of interpretation beforehand.
As the data the computers ingest is unique, they have to apply accuracy and interpret it accordingly as per the tags provided.
As opposed to supervised learning, the unsupervised learning style in ML does not focus on any tags or labels provided beforehand. Rather, the computers are required to interpret data according to the patterns they find relevant as and when they go through the large mass of information. Perhaps the learning style in this type is unsupervised.
Over the course of time, computers improve their accuracy to interpret data and identify relevant patterns. This can be done by the type of reinforcement learning that implies that computers work on the basis of trial and error method, and get rewarded as and when they identify a pattern that takes them closer to their objective.
Herein, no tags or labels are provided beforehand, but the computers find the required patterns by interpreting data over and over again.
Understanding learning styles in Machine Learning is very essential as it helps us to know more about the way this concept works.
The working of Machine Learning can be understood with the help of the flowchart given below. As computers are fed with data every now and then, they build a set of algorithms that helps them to interpret data in the future.
Machine Learning Working Flowchart, Source
However, this process requires training data that enables computers to design algorithms in order to achieve their goals. For instance, if a machine is expected to interpret data and filter names of all the employees working at a specific organization, then the training data for the same is the past records of employees including their names.
This training data will help the computer to interpret it and identify patterns related to the names of the employees.
Furthermore, the interpretation of training data helps the computer to design particular machine learning algorithms that help the computer to learn from data and identify the logical pattern hidden behind it.
Building logical models requires big data to be fed to the computers so that it helps the machine to interpret large amounts of data, interpret it accordingly, and identify relevant patterns for future use. This way, the machines are made to refine their accuracy and speed over time.
Once the machine learning algorithms are designed and the computers are adapted to interpret data, new data is fed and ingested into the machine that leads to output. Even though the algorithms have been designed, there are chances that unique data can be put into the system that pushes computers to design new algorithms.
However, the accuracy and the speed of the system play a major role in this very step and perhaps triggers the process of interpretation which ultimately leads to quality output.
With respect to this flowchart, one can notice that past records and new data are intertwined when it comes to the designing of algorithms. Therefore, historical data can help the analysis of new information with the help of patterns that have already been established.
(Must check: Top Deep Learning Algorithm)
While ML is a boon in the realm of Artificial Intelligence, it has its own share of merits and demerits. Let us now have a look at the various merits and demerits of Machine Learning.
Machine Learning is beneficial because it primarily facilitates efficient data management in computers. By managing vast amounts of data and closely observing it, computers make use of ML and interpret data.
This management is very helpful in the field of technology and has made the process of data management rapid. With the help of numerous methods of ML, computers identify relevant patterns that help humans to carry out otherwise tedious tasks.
With so many applications of Machine Learning, the world of technology revolves around ML. its valuable use in the field is highly commendable. From Natural Language Processing to Artificial Intelligence, ML is omnipresent as it empowers technology to leap forward. With so many benefits of learning machine learning, the concept of AI is highly advanced more than ever.
“Digital assistants: Apple Siri, Amazon Alexa, Google Assistant, and other digital assistants are powered by natural language processing (NLP), a machine learning application that enables computers to process text and voice data and 'understand' human language the way people do. Natural language processing also drives voice-driven applications like GPS and speech recognition (speech-to-text) software.” machine learning examples
Machine Learning is defined as the process wherein machines like computers are made to learn human skills without needing human assistance. This particular merit of ML- its ability to carry out automatic operation has made it a reputed and regarded concept in the realm of technology.
Not only does it work independently, but it also keeps humans away from the process, thus saving time and energy from most of the processes involved in the concept.
“Self-driving cars: Self-driving cars require a machine learning tour de force—they must continuously identify objects in the environment around the car, predict how they will change or move, and guide the car around the objects as well as toward the driver's destination. Virtually every form of machine learning and deep learning algorithm mentioned above plays some role in enabling a self-driving automobile.” Application of ML
(Must check: Top Machine Learning Tools)
While much of the ML process is automated and works with the help of computers, the process of algorithm selection in machine learning is still manual and requires human assistance, and is perhaps one of the biggest limitations of machine learning.
This means that while computers design the algorithms, humans designate the algorithms that are supposed to be included in the interpretation of data. This can be a tedious task as it requires humans to run data through various algorithms and identify the one that works highly well.
While management of data is a forte of ML, the issue of errors still remains a highlighting drawback. Furthermore, the delayed resolution of errors is what keeps the concept of ML from becoming perfect!
This means that even though the errors occur in the process of Machine Learning, the resolution of errors is a much-delayed process due to the strong reliance of machines on algorithms. This particular trait can be a major disadvantage as it can hamper the process of data management.
Lastly, the requirement of extensive resources in ML is a concern for many. Why? Because the interpretation of data can be a time-taking process that requires many other equipment attached to your computer.
That’s right! If you are thinking that your computer alone will be enough for conducting Machine Learning Tutorial, think again! The requirement of extensive resources for ML remains a drawback to date and is a matter of concern for many.
All in all, Machine Learning is a technologically advanced yet essential concept that has empowered the process of data management. Taking into account vast amounts of data, Machine Learning has enabled computers to become the best friends of humans in terms of reliance and results.
(Recommended blog: Machine Learning vs Data Science)
Comparatively error-free and continuously evolving, ML is the fuel for Artificial Intelligence considering that it has such a vast range of applications and uses in the field of technology.
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