Machine learning(ML) is defined as the application and subfield of artificial intelligence which makes a machine behave and think like a human. Machine learning uses data and starts to learn things by itself as the human mind does without the involvement of human interactions and programme code.
In the 21st century, ML has a wide scope in every field. No field is left untouched with ML in today’s era of automation. Machine learning and Artificial intelligence are considered the backbone of this automation.
Latest discuss some of the applications of ML in the modern era:
Personal assistants and robots:
Machine learning provides us with 24-hour assistance. When we come up with the term “personal assistant”, a term that pops into our mind is Google assistant. Google Assistant, Alexa, Siri, Cortana, and chatbots all are the byproduct of ML. Their new feature like face recognition, speech recognition, voice recognition through which we search all if and buts of problems are outcomes of ML
Machine learning is widely used in stock market trading. In the stock market, there is always a risk of ups and downs in shares, so for this machine learning’s long short-term memory neural network is used for the prediction of stock market trends.
Nowadays all filter cameras, tagging suggestions are the results of machine learning image recognition algorithms. Machine learning helps us in image recognition by forming a multilayer structure of each pixel.
Speech Recognition application of machine learning rocks in the IT world as it helps in reducing typing time and coding time of an individual. Thousands of pages of content can be written in a few hours machine learning uses the voice pattern and neural network techniques to translate the speech into words. Nowadays these techniques are used in every search engine and browser with the promotion of a new line “bolane se sab milta hai”.
While surfing on websites like Amazon, Flipkart, Meesho, Zomato, purple and many more, we find a common thing on such platforms that the product and the parcel be ordered in previous visits are shown in recommendations with different varieties without any further searches. All these are the applications of machine learning which makes our task easy.
All the predictions whether it is related to traffic and scenario, are done by machine learning based on the last revealed data and information machine learning try to understand the sequence and pattern of data and predict the result based on these information.
In the pandemic time, this application of machine learning helped mankind with utmost ease- In predicting the number of positive cases in the coming scenario which awakened the authority to take a decision at right time and handle the circumstances with minimum loss.
Email and malware filter:
This application is counted as a relaxation technique for big companies who are receiving thousands of emails every 6 hours of the day. Email and spam filter automatically filters the emails and spam. This data filter filtration is done by records of user visits and his previous behaviour of handling information.
A self-driven car is one of the blessings of machine learning. The self-driving car uses all sub-application of machine learning – whether it is image recognition, voice recognition and predicting the result.
Analysing a scenery of real-time with real-time actions is a big task in human life and when we come up with machine and self-driving cars then it mounts as a big hurdle which requires a big jump to overcome or we can say next to impossible task for machines but machine learning makes it possible by using data analysis structures and artificial intelligence.
Also Read: ALL ABOUT THE METAVERSE
Machine learning/Artificial Intelligence/Deep Learning
Machine learning can’t be isolated from artificial intelligence and deep learning concept by which we are trying to give a human mind to a machine. Artificial intelligence is a self-defined term that means human-made smartness. In artificial intelligence, we make a machine that imitates like a human, thinks like a human or act like a human in different scenarios.
The term artificial intelligence, machine learning and deep learning seems to be very confusing for a non-technical person. Most of the individual things that all three have are the same but there is a vast difference between them.
Deep learning is a subfield of machine learning and machine learning is the subfield of artificial intelligence. Although these three terms have a close connection, however, we can’t interpret them as the same.
ML involves making a machine self-reliant but guidance is required to do so and sometimes it would take years to teach but if we look at the concept of deep learning, it involves the creation of a neural network.
So, if we come up with artificial intelligence, it means thinking and analysing how we make machines that can be thinking by themselves. ML involves how to teach a machine to think themselves using different data algorithms and past data and information.
Deep learning involves analysing the data in deep and figuring out the scenario which includes interconnected neurons that will learn how to react, how to think and how to take actions without human interaction.
Latest understand it with an example:
(Suppose we have a flashlight and we teach an ML model that whenever someone says “dark” the flashlight should be on, now the machine learning model will analyze different phrases said by people and it will search for the word “dark” and as the word comes the flashlight will be on but what if someone said “I am not able to see anything the light is very dim”, where the user wants the flashlight to be on but the sentence does not the consist the word “dark” so the flashlight will not be on.
That’s where deep learning is different from machine learning. If it were a deep learning model, it would be on the flashlight. A deep learning model can learn from its method of computing.) Source.
Advantages of machine learning:
Step towards automation
Machine learning brought a world of automation where everything is self-driven and self-reliant.
Nowadays, all the stuff is done by the machines on their own and humans are only involved to operate and train them and the rest is done by AIML.
Analyzing the pattern
Machine learning coats recognise the pattern data and data flow with such great ease. Analysing and interpreting the heap of data and predicting the result with great accuracy and correctness is a complicated task for an individual, however, for machine learning, it’s a Doodle.
An ERA of automation, the year 2022, machine learning is used in every sector whether it is of Sales and operations planning tools, Product Analytics, Stock Forecasting, Fraud Prevention and many more. In every field machine learning has marked its footprints with firm steps.
In 2021, millions of data were generated each day. Resultantly, handling such big data is a difficult task to carry out but through machine learning, it’s become a cinch job to do.
ML provides your environment to perform different jobs simultaneously without any human intervention ML offer multidimensional and multi-variety utilities for a particular task.
Scope of improvement
Artificial intelligence and ML are two terms whose evolution is never going to end. We can’t reach their infinite level there is always scope of improvement in these fields and which evolved new opportunities and ideas.
AIML opens an innumerable gateway of new job opportunities to explore and reconstruct the sectors of the world and bring a market technical skills and talent. Now, we have more than thousands of new areas to work in.
Reduce time and complexity
The introduction of ML has shortened the period consumed to get over the specified job and also reduces the chances of life and works of the thought process of an individual as the whole work is performed by automata which reduces chances of human error too and make the task biddable.
Disadvantages of machine learning
Training a machine is not a child’s play. It includes brilliancy and the efforts of an individual. In day-to-day life also teaching involves a high thought process and if we talk about teaching a machine then selecting the right content to teach becomes a big headache. Selecting algo and data statics to make a machine, a human is such a complicated task to do.
After learning when ML models are tested then they come out with vast errors and due to their dealing with big data it seems to be impossible to debug the whole content and programmes.
Data acquisition and inconsistency
Teaching a machine involves a lot of data with a time-to-time update. So, that machine can understand the current real-time scenario but some time when the model does not get data in a consistent format then there is a huge chance of wrong interpretation of data which results in demolishment in its decision and control.
Time taking and massive resources
Evolving a single model of machine learning required ample resources with high acceptance and the time taken to learn a specified work by machines also takes years to complete.
Excessive use may harm mankind:
The machine learning model replaces the human community in the workspace to such an extent that its future scope sometimes gives a warning alarm.
If we have a look at a highly automated future, then it became a matter of concern to give employment opportunities. This is one of the big drawbacks of ML to exploit the human race in the employment sector as it’s not everyone’s cup of tea to learn the high skills of the technical era.
As we are well known with the fact that discovering new things can’t stop, similarly the evolution of machine learning can’t be halted. Every day we have some and some evolution to tell and now it’s all about today of ML.