Let's start learning “Machine Learning”
What is Machine Learning? History of Machine Learning.
Before starting this blog, I want to share an incident in my life when I realize what computers are. I was in 4th grade when my computer teacher asked the question “Are computers intelligent???”. I was the only one in my class to answer that “Yes, computers are intelligent”. But my teacher said that I m wrong and claims that “Computers are not intelligent, rather they use human intelligence to perform tasks”. This made me curious, I asked myself “cant computers think by themself??”. I have this answer now, that is “Artificial intelligence”.
Introduction
We are living in a world of human and machines, humans have been learning and evolving from their past experiences for millions of years. On the other hand, the era of machine and robots have just begun. In today's world, the robots need to be programmed before they actually follow the instructions, but what if machines started to learn on their own, and this is when Machine learning comes into the picture.
Definition of Machine Learning
Machine Learning is a subfield of artificial intelligence, where we train our models that can learn and make decisions/predictions based on past experience. These models are designed to learn and improve over time when exposing to new data. Today, machine learning algorithms enable computers to communicate with humans, autonomously drive cars, write and publish sport match reports, and recommend relevant products. Machine learning is severely impacting most industries and the jobs within them, which is why every manager should have at least some grasp of what machine learning is and how it is evolving.
History of Machine Learning
In 1946 the first computer system ENIAC was developed. At that time the word ‘computer’ meant a human being that performed numerical computations on paper and ENIAC was called a numerical computing machine. This machine was manually operated, i.e. a human would make connections between parts of the machine to perform computations. The idea at that time was that human thinking and learning could be rendered logically in such a machine.
In 1950 Alan Turing proposed a test to measure its performance which is called the “ Turning Test”. The Turing test is based on the idea that we can only determine if a machine can actually learn if we communicate with it and cannot distinguish it from another human. Although, there have not been any systems that passed the Turing test many interesting systems have been developed.
In 1957 Frank Rosenblatt invented the Perceptron at the Cornell Aeronautical Laboratory. The Perceptron is a very simple linear classifier but it was shown that by combining a large number of them in a network a powerful model could be created.
Mathematician Ivakhnenko and associates including Lapa arguably created the first working deep learning networks in 1965, applying what had been only theories and ideas up to that point.
In 1979–80 A recognized innovator in neural networks, Fukushima is perhaps best known for the creation of Neocognitron, an artificial neural network that learned how to recognize visual patterns. It has been used for handwritten character and other pattern recognition tasks, recommender systems, and even natural language processing.
In the early 90’s Machine Learning became very popular due to the intersection of Computer Science and Statistics. This synergy resulted in a new way of thinking in AI: the probabilistic approach. In this approach uncertainty in the parameters is incorporated in the models. The field shifted to a more data-driven approach as compared to the more knowledge-driven expert systems developed earlier.
Currently, the study of Machine Learning has grown from the efforts of a handful of computer engineers exploring whether computers could learn to play games and mimic the human brain, and a field of statistics that largely ignored computational considerations, to a broad discipline that has produced fundamental statistical-computational theories of learning processes.
This was a very basic definition of Machine Learning. I will be coming soon with the application and type of machine learning part in my next blog.
References
[1]. “Machine Learning textbook”. www.cs.cmu.edu. Retrieved 2020–05–28
[2] Book: Master machine learning algorithms by Jason Brownlee
[3]. Wikipedia link: https://en.wikipedia.org/wiki/Machine_learning
[4]. https://data-flair.training/blogs/machine-learning-tutorial
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