What is Machine Learning?

Two definitions of Machine Learning are offered. Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition.

Tom Mitchell provides a more modern definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

Example: playing checkers.

E = the experience of playing many games of checkers

T = the task of playing checkers.

P = the probability that the program will win the next game.

In general, any machine learning problem can be assigned to one of two broad classifications:

Supervised learning and Unsupervised learning.

1. Supervised Learning: In a supervised learning model, the algorithm learns on a labeled dataset, to generate expected predictions for the response to new data.

eg: Very classy example is house price prediction, we first need data about houses such as; square foot, no. of rooms, the house has a garden or not, and so on features. We then need to know the prices of these houses ie; class labels. Now data coming from thousands of houses, their features, and prices, we can now train a supervised machine learning model to predict a new house’s price based on past experiences of the model.

Supervised Learning is of two types:

a) Classification: In Classification, a computer program is trained on a training dataset, and based on the training it categorizes the data in different class labels. This algorithm is used to predict the discrete values such as male|female, true|false, spam|not spam, etc.

Eg; Email spam detection, speech recognition, identification of cancer cells, etc.

Types of Classification Algorithms:

Naive Bayes classifier
Decision Trees
Logistic Regression
K-Nearest Neighbours
Support vector machine
Random forest classification

b) Regression: The task of the regression algorithm is to find the mapping function to map input variables(x) to the continuous output variable(y). Regression algorithms are used to predict continuous values such as price, salary, age, marks, etc.

Eg; Weather prediction, house price prediction, fake news detection, etc.

Types of Regression Algorithms:

Simple linear Regression
Multiple linear Regression
polynomial Regression
Decision Tree Regression
Random forest Regression
Ensemble Method

2. Unsupervised Learning: In an unsupervised learning model, the algorithm learns on an unlabeled dataset and tries to make sense by extracting features, co-occurrence, and underlying patterns on its own.

Eg; Anomaly detection, including fraud detection. Another example is Opening emergency hospitals to the maximum prone to accident areas. K-means clustering will group these locations of max prone areas into clusters and define a cluster center(ie;hospital) for each cluster(ie;accident prone areas).

Types of Unsupervised Learning:

Clustering
Anomaly detection
Association
Autoencoders
Latent variable models
Neural Networks

This article was published as a part of the Data Science Blogathon.

Introductory

Artificial Intelligence is purely math and scientific exercise but when it becomes computational, it starts to solve human problems.

Machine Learning is a subset of Artificial Intelligence. ML is the study of computer algorithms that improve automatically through experience. ML explores the study and construction of algorithms that can learn from data and make predictions on data. Based on more data, machine learning can change actions and responses which will make it more efficient, adaptable, and scalable.

Deep Learning is a technique for implementing machine learning algorithms. It uses Artificial Neural Networks for training data to achieve highly promising decision making. The neural network performs micro calculations with computational on many layers and can handle tasks like humans.

Types of Machine Learning

1. Supervised Learning: In a supervised learning model, the algorithm learns on a labeled dataset, to generate expected predictions for the response to new data.

Eg; For House price prediction, we first need data about houses such as; square foot, no. of rooms, the house has a garden or not, and so on features. We then need to know the prices of these houses ie; class labels. Now data coming from thousands of houses, their features, and prices, we can now train a supervised machine learning model to predict a new house’s price based on past experiences of the model.