A Quick History of Machine Learning:
It was in the 1940s when the first manually operated computer system, ENIAC (Electronic Numerical Integrator and Computer), was invented. At that time the word “computer” was being used as a name for a human with intensive numerical computation capabilities, so, ENIAC was called a numerical computing machine!
In 1943 when neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper about neurons, and how it works. They decided to create a model of this using an electrical circuit, and therefore the neural network was born.
In 1950, Alan Turing created the world-famous Turing Test. This test is fairly simple – for a computer to pass, it has to be able to convince a human that it is a human and not a computer
1952 saw the first computer program which could learn as it ran. It was a game which played checkers, created by Arthur Samuel.
Frank Rosenblatt designed the first artificial neural network in 1958, called Perceptron. The main goal of this was pattern and shape recognition.
In 1990, the intersection of computer science and statistics gave birth to probabilistic approaches in AI.
Well, you may say it has nothing to do with learning?! WRONG, from the beginning the idea was to build a machine able to emulate human thinking and learning.
What is Machine Learning?
According to Arthur Samuel
“Machine Learning algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.”
Machine learning (ML) is a category of an algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.
Types of Machine Learning
Machine learning can be classified into 3 types of algorithms.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
For instance, suppose you are given a basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this:
- If the shape of an object is rounded and depression at the top having color Red then it will be labeled as –Apple.
- If the shape of an object is a long curving cylinder having color Green-Yellow then it will be labeled as –Banana.
Now suppose after training the data, you have given a new separate fruit say Banana from the basket and asked to identify it.
Since the machine has already learned the things from previous data and this time have to use it wisely. It will first classify the fruit with its shape and color and would confirm the fruit name as BANANA and put it in the Banana category. Thus the machine learns the things from training data(basket containing fruits) and then applies the knowledge to test data (new fruit).
Unsupervised learning is the training of an artificial intelligence (AI) algorithm using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.
For instance, suppose it is given an image having both dogs and cats which have not seen ever.
Thus the machine has no idea about the features of dogs and cats so we can’t categorize it in dogs and cats. But it can categorize them according to their similarities, patterns, and differences i.e., we can easily categorize the above picture into two parts. The First may contain all pics having dogs in it and the second part may contain all pics having cats in it. Here you didn’t learn anything before, which means no training data or examples.
Reinforcement learning is an area of Machine Learning. Reinforcement, it is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.
Main points in Reinforcement learning –
- Input: The input should be an initial state from which the model will start
- Output: There are much possible output as there are a variety of solution to a particular problem
- Training: The training is based upon the input, the model will return a state and the user will decide to reward or punish the model based on its output.
- The model keeps continues to learn.
- The best solution is decided based on the maximum reward.
Conclusion: What is Machine Learning
Machine learning has rapidly grown in the field of computer science. It has applications in nearly every other field of study and had already been implemented commercially because machine learning had successfully solved the problems that were too difficult or time consuming for humans to solve.
In short, we can describe machine learning in general terms, various models are used to learn patterns in data and make accurate predictions based on the patterns it observes.
Finally, when it comes to learning and practicing each machine learning technique then The subject is vast, it means that there is width, but if you consider the depth, each topic can be learned in a few hours. Each topic is independent of each other. You need to take into consideration one topic at a time, learn it, practice it and implement the algorithm/s in it using a language choice of yours. This is the best way to start studying Machine Learning.