Machine learning — Introduction

Hi.

This is my first post in this publication. I am a ML enthusiast and I am still learning it. I think the best way to learn is to share the knowledge. So, I will be posting an article on Machine Learning every Tuesday. The content will be focused on topics related to ML, Statistics, and Probability. I will try to keep the content as simplified and interesting as possible.

Let’s get started.

A formal definition

There is no concrete definition of Machine learning. But in the words of Arthur Samuel,

Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed.

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning:

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.

An Example
In more simpler terms, ML refers to a vast set of tools or algorithms for understanding data. Suppose, you are given a training dataset which contains size of the houses in a region and their corresponding prices.

Your task is to use the training data set and build a statistical model which can estimate the price of a house of any given size. But, the question here is :

How will you build a model?

This is what we are going to learn throughout in Machine learning. We shall try to come up with various models depending upon features (size of the house) and target outputs (price of the house).

Machine Learning algorithms are classified as supervised or unsupervised. Broadly speaking, supervised learning involves building a statistical model for predicting, or estimating, an output based on one or more variables. Problems of this nature occur in fields as diverse as business, medicine, astrophysics, and public policy. With unsupervised statistical learning, there are inputs but no supervising output; nevertheless we can learn relationships and structure from such data. Clustering is one such example.

We will go into the details of each one of them in the subsequent posts.

Who are using Machine Learning?

1. Finance — Banks and other businesses in the financial industry use machine learning technology to identify important insights in data, and to prevent fraud.
2. Healthcare — Machine learning is a fast-growing trend in the healthcare industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnosis and treatment.
3. Online shopping — Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history.
4. Oil and Gas — Finding new energy sources, analyzing minerals in the ground, predicting refinery sensor failure and streamlining oil distribution to make it more efficient and cost-effective.
5. Transportation — Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.

Next : Supervised Learning

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