Machine learning gives computers the power to automatically learn and improve from experiences. Machine learning elevates computers’ ability in that, it helps it discover how they can perform tasks, even when they are not explicitly programmed for them. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves so that they carry out certain tasks.
Based on the contextual data provided the machine makes predictions by identifying patterns. All these happen by the learning picked by machine due to data observation or instructions given to it. Learning may not be needed if the algorithm of our system is self-sufficient to inform the machine of the action mode with necessary steps for simple tasks. For advanced tasks, users find it difficult to manually create the needed algorithms.
Primarily, the computers are programmed to learn automatically and adjust actions. However, in real life, it has been found that training the machine to develop its own algorithm, rather than having human programmers is more effective. For instance, when we use classic algorithms of machine learning, we consider the text as a sequence of keywords whereas, in ML, an approach based on semantic analysis mimics the human ability to comprehend the text.
Machine learning provides various routes to teach computers to perform tasks even where an unsatisfactory or incomplete algorithm is concerned. In cases where several potential answers are possible, one o+f the approaches tells the system some of the correct answers, used as training data for the computer to improve the future algorithm it uses.
A little bit of history for perspective
Arthur Samuel an American IBMer coined ’Machine Learning’ in 1959 was a pioneer in the field of computer gaming and artificial intelligence. Interest in pattern recognition has been growing since the 1970s. However, in 1981, a report on teaching strategies to neural network learns to recognize 26 letters, 10 digits, and 4 special symbols from a computer terminal was a seminal moment in the development of ML.
Key Elements of Machine Learning
There are millions of new machine learning algorithms developed each year. Every machine learning algorithm has three components:
Representation: How to represent knowledge. This includes presenting decision trees, sets of rules and instances, graphical models, and neural networks just to name some of the types..
Evaluation:The way to evaluate the Hypotheses. This involves prediction and recall, squared error, likelihood and posterior probability, cost, and margin, just to name a few.
Optimization:The way the hypothesis is generated, is known as the search process. For example, combinatorial optimization, convex optimization, constrained optimization.
All MLs is a framework that helps understand all other algorithms using combinations of these above three components.
Types of Learning
There are four types of machine learning:
Supervised learning is Training data that includes desired outputs. In this approach, the machine learns by example under human supervision.
Unsupervised learning happens when training data does not include the desired outputs. In Unsupervised learning, algorithms are tasked with identifying patterns in data, trying to spot similarities and variances, and categorize them into segments. An example is clustering. Its quality of learning is not guaranteed.