Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. Machine Learning is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
An introduction to Machine Learning
The term Machine Learning was coined by Arthur Samuel in 1959, an American pioneer in the field of computer gaming and artificial intelligence and stated that “it gives computers the ability to learn without being explicitly programmed”.
And in 1997, Tom Mitchell gave a “well-posed” mathematical and relational definition that “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
Machine Learning is the latest buzzword floating around. It deserves to, as it is one of the most interesting subfields of Computer Science. So what does Machine Learning really mean?
Let’s try to understand Machine Learning in layman terms. Consider you are trying to toss a paper to a dustbin.
After the first attempt, you realize that you have put too much force in it. After the second attempt, you realize you are closer to target but you need to increase your throw angle. What is happening here is basically after every throw we are learning something and improving the end result. We are programmed to learn from our experience.
Getting started with Machine Learning
This article discusses the categories of machine learning problems, and terminologies used in the field of machine learning.
Types of machine learning problems
There are various ways to classify machine learning problems. Here, we discuss the most obvious ones.
1. On basis of the nature of the learning “signal” or “feedback” available to a learning system
- Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. The training process continues until the model achieves the desired level of accuracy on the training data. Some real-life examples are:
- Image Classification: You train with images/labels. Then in the future you give a new image expecting that the computer will recognize the new object.
- Market Prediction/Regression: You train the computer with historical market data and ask the computer to predict the new price in the future.
- Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. It is used for clustering populations in different groups. Unsupervised learning can be a goal in itself (discovering hidden patterns in data).
- Clustering: You ask the computer to separate similar data into clusters, this is essential in research and science.
- High Dimension Visualization: Use the computer to help us visualize high dimensional data.
- Generative Models: After a model captures the probability distribution of your input data, it will be able to generate more data. This can be very useful to make your classifier more robust.
As you can see clearly, the data in supervised learning is labelled, whereas data in unsupervised learning is unlabelled.
- Semi-supervised learning: Problems where you have a large amount of input data and only some of the data is labeled, are called semi-supervised learning problems. These problems sit in between both supervised and unsupervised learning. For example, a photo archive where only some of the images are labeled, (e.g. dog, cat, person) and the majority are unlabeled.
- Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.
Machine Learning Course in Delhi
Aptron Provides the best Machine Learning Course in Delhi in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. Indicating the increased adoption of Machine Learning among companies. The demand for Machine Learning engineers is expected to grow by 60-percent.