Intoduction to Machine Learning

Machine learning is the ability of machine to learn from data . Does it mean if you provide data to computer it will learn itself , it doesn't work like that.

“How would you define machine learning ?” then

Machine learning is a subset of artificial intelligence (AI) that deals with the extracting of patterns from data, and then uses those patterns to enable algorithms to improve themselves with experience. This type of learning can be used to help computers recognize patterns and associations in massive amounts of data, and make predictions and forecasts based on its findings.

“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.” by Tom Mitchell

You must be wondering Why do we need machine to learn ?

Humans may be smart, but we often just can’t see well enough. There’s a lot we might want to know about our business, but the patterns we need are hidden in dense data. Machine learning lets us teach a computer to look at the same data that we’re looking at, and to then derive patterns and connections that we can’t see.

Types of Algorithms used in Machine learning.

Machine Learning systems can be classified according to the amount and type of supervision they get during training. There are three major categories: supervised learning, unsupervised learning and Reinforcement Learning.

Supervised learning

In supervised learning, the training data you feed to the algorithm includes the desired solutions, called labels. In this we try to predict dependent variable with the help of independent variables. This supervised learning algorithm is further divided into Classification and Regression depending upon the type of dependent variables ,

if our dependent variable can take infinte number of values then we say its is a case of regression wereas if the dependedent variable classify some thing eg ; good ,bad,spam hems, then we can say its the case of classification.

Note that some regression algorithms can be used for classification as well, and vice versa. For example, Logistic Regression is commonly used for classification, as it can output a value that corresponds to theprobability of belonging to a given class (e.g., 20% chance of being spam).

Unsupervised learning algorithms

In unsupervised learning, as you might guess, the training data is unlabeled. The system tries to learn without a teacher.

For example, say you have a lot of data about your blog’s visitors. You may want to run a clustering algorithm to try to detect groups of similar visitors . At no point do you tell the algorithmwhich group a visitor belongs to: it finds those connections without your help. For example, it might notice that 40% of your visitors are males who love comic books and generally read your blog in the evening, while 20% are young sci-fi lovers who visit during the weekends, and so on. If you use a hierarchical clustering algorithm, it may also subdivide each group into smaller groups. This may help you target your posts for each group.

Reinforcement Learning

Reinforcement Learning is a very different beast. The learning system, called an agent in this context, can observe the environment, select and perform actions, and get rewards in return (or penalties in the form of negative rewards). It must then learn by itself what is the best strategy, called a policy, to get the most reward over time. A policy defines what action the agent should choose when it is in a given situation.

This blog is just an overview of machine learning , stay connected for more deep knowledge of Machine learning algorithms.