Introduction to Machine Learning

Updated: May 11, 2020

In this blog, I will tell you about introduction of machine learning and the basic application and usage of machine learning.

“Machine Intelligence is the last invention that humanity will ever need to make..” - Nick Bostrom

Machine learning is about extracting Knowledge from data.It is research field at the intersection of statistics, artificial intelligence and computer science, also known as predictive analytics or statistical learning. The application of machine learning methods has in recent years become famous in everyday life. From automatic recommendations of which movie to watch, to what food to order or which product to buy, many modern websites and devices have machine learning algorithms at their core.

When look at complex websites like Facebook, Amazon and Netflix, it is very likely that every part of the website you are looking at contains multiple machine learning models.

Why Machine Learning ?

In the early days of "intelligent" applications, many systems used hand-coded rules of "if" and "else" decisions to process data or adjust to user input. Think of spam filter whose job is to move an email to spam folder. You could make up a black-list of words that would result in an email marked  as spam. This would be  an example of using an expert  designed rule system to design an intelligent system. Designing kind of manual design of decision rules is feasible for some applications, in particular for those applications in which humans have a good understanding of how a decision should be made. However, using hand-coded rules to make decisions has two major disadvantages:

  1. The logic required to make a decision is specific to a single domain and task. Changing the task even slightly might require a rewrite of the whole system.

  2. Designing rules requires a deep understanding of how a decision should be made by a human expert.

Using machine learning, however, simply presenting a program with a large collection of images of faces is enough for an algorithm to determine what characteristics are needed to identify a face.

Problems that Machine learning can solve

The most successful kind of machine learning algorithms are those that automate a decision making processes by generalizing from known examples. In this setting, which is known as a supervised learning setting, the user provides the algorithm with pairs of inputs and desired outputs, and the algorithm finds a way to produce the desired output given an input.

Going back to our example of spam classification, using machine learning, the user provides the algorithm a large number of emails (which are the input), together with the information about whether any of these emails are spam (which is the desired output). Given a new email, the algorithm will then produce a prediction as to whether or not the new email is spam.

Examples of machine learning tasks include:

  • Identifying the ZIP code from handwritten digits on an envelope.

  • Determining whether or not a tumor is benign on medical science.

  • Detect fraud activity in credit card transaction.

  • Segmenting customers into groups with similar preferences.

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