Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.
It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications, and its importance.
To help you understand this topic I will give answers to some relevant questions about machine learning.
But before we answer these questions, it is important to first know about the history of machine learning.
A brief history of machine learning
You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”.
In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the Perceptron Model.
In 1959 Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines.
In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition.
Gerald DeJonge, in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information.
During the 1990s work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyze large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning.
Follow the blog on Machine learning Interview questions if you want to know what are some important machine learning interview questions.
Now that we know about the origin and history of ml, let us start by answering a simple question — What is Machine Learning?
What is Machine Learning?
Have you ever wondered how Facebook’s ‘People you may know’ feature always provide you with a genuine list of people that you actually know in real life and with whom you should connect with on Facebook as well? How does Facebook come to know about this? How are they doing this recommendation?
Well, Machine Learning is an answer to this question.
Machine learning definition according to Tom Mitchell:
“The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience”
In simpler words, machine learning is the field of computer science which makes the machine capable of learning on its own without being explicitly programmed.
The point to be noted here is that ML algorithms can learn on its own from past experiences, just like humans do. When exposed to new data, these algorithms learn, change and grow by themselves without you needing to change the code every single time. So basically, what happens is that, instead of you writing the code every single time for a new problem, you simply feed the data to the ml algorithm and the algorithm/machine builds the logic and provides results based on the given data.
Initially, the results obtained might not be of high accuracy but, over time the accuracy of ml algorithms become higher as it continuously performs tasks.
How do Machine Learning algorithms work?
Machine Learning algorithms utilize a variety of techniques to handle large amounts of complex data to make decisions. These algorithms complete the task of learning from data with specific inputs given to the machine. It’s important to understand how these algorithms and a machine learning system as a whole work, so that we can get to know how these can be used in the future.
Originally published at https://www.verzeo.in.
