Machine Learning has really been the buzz in software industry for the last 7 years, but what exactly is machine learning and why should you care about it, keep reading the blog to find the answers. What is Machine Learning? Machine learning is a sub-field of artificial intelligence. Its goal is to enable computers to . learn on their own A machine’s learning algorithm enables it to in observed data, that explain the world, and without having explicit pre-programmed rules and models. identify patterns build models predict things The term was coined by in 1959, an American pioneer in the field of computer gaming and artificial intelligence and stated that Machine Learning Arthur Samuel “It gives computers the ability to learn without being explicitly programmed”. And in 1997, gave a “well-posed” mathematical and relational definition that Tom Mitchell “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.” Different types of Machine Learning :- Supervised Learning. Unsupervised Learning. Reinforcement Learning. Machine Learning Types 1. Supervised Learning :- In supervised learning, we are given a data set and should look like, having the idea that there is a relationship between the input and output. already know what our correct output Two types of Supervised Learning :- — Estimate continuous values (Real valued output) Regression — Identify a unique class (Discrete values, Boolean or Categories) Classification 1.1 Regression :- models a target prediction value based on independent variables. It is mostly used for finding out the and . Regression can be used to estimate/ predict (Real valued output). Regression relationship between variables forecasting continuous values For example Given a picture of a person, we have to predict the age on the basis of the given picture . : 1.2 Classification :- means to the output into a class. If the data set is or then it is a classification problem. Classification group discrete categorical For example Given data about the sizes of houses in the real estate market, making our output about whether the house “sells for or than the asking price” i.e. Classifying houses into two discrete categories. : more less 2. Unsupervised Learning :- It allows us to approach problems with little or no idea about what our results look like. We can from data where we don’t necessarily know the effect of the variables. derive structure We can derive this structure by the data based on relationships among the variables in the data. clustering 2.1. Clustering :- C is the task of grouping a set of objects in such a way that objects in the same group (called a ) are more similar (in some sense) to each other than to those in other groups (clusters). lustering cluster For example : Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into or related by different variables, such as lifespan, location, roles, and so on. groups that are somehow similar 3. Reinforcement Learning :- Reinforcement Learning is about taking suitable actions to in a particular situation. It is employed by various software and machines to find the or path to take in a specific situation. maximize reward best possible behavior Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it, so the model is trained with the correct answer itself whereas in reinforcement learning, and the decides what to do in order to perform the given task. In the absence of training data set, it is bound to . there is no answer reinforcement agent learn from its experience Applications of Machine Learning :- Virtual Personal Assistants. Predictions while commuting.Videos Surveillance.Social Media Services. Email Spam and Malware Filtering.Online Customer Support. Search Engine Result Refining. Product Recommendations. Online Fraud Detection.

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