We often hear a term name machine learning from different resources that this application is based on ML. So what exactly is machine learning? ML is basically a sub-branch of AI (artificial intelligence).
Machine learning by definition means that study of such computer algorithms which improve automatically through experience. Of course, these algorithms are not just a few lines of codes but a very complex structure which has the ability to improve over time as it processes more data. 10 Companies Using Machine Learning in Cool Ways.
Importance of Machine Learning:
Almost all of the today’s most popular applications use ML, for example, Facebook uses a ML algorithm to suggest you the type of videos you watch or to recommend you friends which have things common with you or like goggle which also uses machine learning to show us our search results. You would have heard that goggle searches the web based on your search history and that also is done through ML. Top 5 Emerging Technologies to watch in 2021.
Types of Machine learning Algorithms:
There are many types of algorithms used for machine learning but the most popular of them are supervised learning and unsupervised learning.
In supervised learning, you provide data to the machine first to process and when a machine has to make a decision on the input value given by the user. The machine makes the decision by using the data that you provided first meaning you supervised the machine first to make the decisions.
Example of supervised learning is like you give pictures of the dog to the machine. And tell it that it’s a dog and machine process that data. And no when you show them the picture of dog machine will know that it’s a dog. If you show a picture of a cat machine will know that it’s not a dog. But the machine will also not know what kind it. Because the machine doesn’t have the data on a cat.
Hats one general example and an actual algorithm are much more complex than that but just to give you a gist of supervised learning.
In unsupervised learning you do not need to provide any kind of data prior to making a decision. It works by collecting data and then making decisions based on that collected data that’s why unsupervised learning is unpredictable.
Like in the above example when you show the picture of cat machine does not know it’s the picture of a cat but if a machine has unsupervised learning algorithm working on it it will save the picture of a cat and next time you show the picture of a cat the algorithm will know that it’s a cat.
That’s how unsupervised learning algorithm works but just as I said in case of supervised learning this example is quit generally the actual unsupervised algorithm is much more complex and complicated than that. Blockchain: Pros and Cons. What industries can get benefits? How exactly?
Impact of ML:
The impact of ML in our daily lives is more than you can imagine. ML has found its applications in almost all fields of science some of them are as follows:
Weather and Climate Prediction:
You will be surprised to know that the weather forecast we see every day uses machine learning algorithms to predict the weather. It uses unsupervised ML algorithm to predict it.
Smart Traffic Light Signals:
The modern traffic light signals also function on the base of machine learning they predict the roads with high traffic and roads with low traffic and operate according the flow of predicted traffic. Like road with small traffic will have more red signals and road with more traffic will get less red signals to avoid jam.
All the applications which have a recommendation system use machine learning algorithms to recommend them their services based on their search history which uses takes up data from the history and compute recommendations for them examples are like Netflix seasons recommendations or YouTube videos recommendations. How will virtual reality technology change us?
Now we would like to conclude our discussion on machine learning and we hope that you now have some knowhow of ML, how it works and its applications. The applications of ML are still increasing and its knowledge is being applied to predict the unknown. Thank you.