In this era of technology, everyone’s favorite topic seems to be artificial intelligence and Machine learning.
From science fiction movies about AI destroying the whole world to Artificial Intelligence being used to save lives in the field of medicine, machine learning is surely the hot topic of the decade.
So the question arises. What is machine learning and how does it work?
What is Learning?
We humans use different techniques to learn new things.
We memorize facts and hope they will retain enough in our memory to ace that test we’ve been studying so hard for.
This is known as declarative knowledge.
A better way to learn can be by deducing new information we have acquired from what we already know.
This is called imperative knowledge.
Machine learning works similarly.
A set of examples or data is fed into the system.
The system then infers something about the process that generated the data.
Artificial intelligence will now use an interface to make predictions about the new (previously unseen) data, which is also known as test data.
What is Machine learning?
Machine Learning in simple words refers to Artificial Intelligence learning automatically from data and experience without any human intervention or programming.
This field of science has expanded drastically over the past decade. Today machine learning can be seen anywhere and everywhere.
AlphaGo, which is a machine learning system from DeepMind (a Google-owned artificial intelligence company) defeated Se-dol, a world-class Go player.
By using machine learning, you can make better predictions about what will happen in the future and learn more about how the world works. This means you can use machine learning to solve real-world problems. In the systems where you need to implement and track active machine learning, you need model operations. It’s an undeniable fact that one can learn Machine learning easily by enrolling in someML Courseoffered by some reputed Institutes.
Model operation is essential for maintaining the accuracy of machine learning models. Next, it is vital to ensure the model’s performance. There are several reasons for this. First, the model needs to produce accurate results. Next, it’s useful for the model to be updated promptly. Because when an organization releases the next patch, the system needs to keep working on the previous version.
Who doesn’t watch Netflix or stream Youtube videos?
All the recommendations you ever get on Netflix or Youtube and even Amazon were made possible using artificial intelligence.
Other things such as drug discovery, character recognition of human handwriting, TWO SIGMA, a hedge fund in New York extensively use Artificial intelligence and Machine Learning.
Voice assistants such as Siri, Alexa, and Bixby, assisted driving and completely autonomous driving, face recognition in your phone or FaceBook, and even cancer diagnosis are made possible due to machine learning.
In summary, Artificial intelligence can do all of these tasks by learning and improving from the past tasks it has performed.
How is Machine Learning different?
In traditional programming, a program is written for the computer which then takes in data, analyses it, and gives some appropriate output.
Machine learning on the other hand deals with giving the computer output along with the data.
The machine learning algorithm will itself produce a program.
This program will be used to infer new information.
This creates a loop of the machine making new programs and then using those programs to solve more problems.
The paradigm of Machine Learning
The basic paradigm on which machine learning works can be summarized as follows:
Artificial Intelligence will observe a set of examples ( the data you fed in it). It will then infer something about the data generation process. Now the machine learning will make predictions about the new (previously unseen data).
There are two variations to this paradigm:
- The supervised paradigm
- The unsupervised paradigm
In a supervised paradigm, the system is given a set of features or labeled pairs.
The system then finds a rule to predict the labels associated with a previously unseen input.
An unsupervised paradigm refers to a given set of features that are not labeled. The system groups then into what are known as “natural clusters”.
Benefits of Machine Learning
Using machine learning you can review a large amount of data in a small amount of time and recognize patterns, a human might not be able to see.
Secondly, since the whole process is automated, no human intervention is required.
Artificial intelligence simply learns by itself and improves itself.
As the algorithm improves itself, it gets more accurate and efficient.
Furthermore, it can handle multi-dimensional data easily.
Due to all these advantages, machine learning company offer a wide application from healthcare to E-shopping.
Disadvantages of Machine Learning
There are two flip sides to a coin.
Despite all the benefits, there are many limitations that machine learning is yet to overcome.
Machine learning required lots of good data to process and learn from, All-in-all this can be a very time-consuming process.
Another setback is the algorithm you choose for your process for the results to be interpreted accurately.
Machine Learning is also susceptible to many errors.
Your biased data might end up giving you a biased result.
What’s worse is that these results might go un-noticed and when they are finally spotted it would be extremely hectic and time-consuming to find the source of error.
Applications of Machine Learning
One of the most common and widely used applications of machine learning is image recognition from Facebook and character recognition where artificial intelligence identifies characters from human writing. Text to pdf software is an example of character recognition.
Another great application is speech recognition, commonly used by voice assistance in smartphones.
Medical diagnosis has become a lot easier using machine learning.
From solving diagnostic and prognostic problems to accurately identifying the disease. Machine learning has truly improved the efficiency and quality of medical care.
Machine learning has a vast application in finances, index arbitrage strategy, and stock predictions.
Moreover, machine learning associations can be used in digital marketing to increase engagement with customers and making relevant personalizations.
Machine learning is extensively being used in the education industry in developed countries.
It is an effective way to keep up to date with the progress being made by individual students in the class. Teachers can use machine learning to ensure no student gets left out and that everyone firmly grasps the concept they are explaining.
Machine learning is also being used in search engines to manage favorites and give recommendations. Google and other voice assistants being used in smart devices are a good example.
Future of Machine learning
With self-improving and learning abilities, machine learning is only going to improve in the future, making feats that appear improbable completely possible.
From improved unsupervised algorithms to increased adoption to quantum computing, enhanced personalization, improved cognitive services, and robots, the dawn of Machine learning are just at our doorstep and its applications are only going to increase in the future.
Best Machine Learning Websites:
Some of the best machine learning websites are:
- Talentica Software
- AE Studio
- Blackburn Labs
- Beyond Analysis
Whether you are in favor of technological advances making lives easier or fearful of what AI has to bring in the future, machine learning is for sure a field to look out for in the future.