**Machine learning & the need for it** à

Machine learning is a sub field of Artificial Intelligence, in which a computer system is fed with algorithms that are designed to analyze and interpret different types of data on their own. These learning algorithms obtain the analysis ability when they are trained for the same using sample data.

It comes in handy when the amount of data to be analyzed is very large & out of human limits. It can be used to arrive at important conclusions & make important decisions.

Some important fields where it is being implemented:

- Cancer treatment-

Chemotherapy, which is used in killing cancer cells poses the danger of killing even the healthy cells in the human body. An effective alternative to chemotherapy is radiotherapy which makes use of machine learning algorithms to make the right distinction between cells.

- Robotic surgery-

Using this technology, risk free operations can be performed in parts of the human body where the spaces are narrow & the risk of a doctor messing up the surgery is high. Robotic surgery is trained using machine learning algorithms.

- Finance-

It is used to detect fraudulent bank transactions within seconds for which a human would take hours to realize.

The utility of Machine learning is endless & can be used in multiple fields.

**What does one learn in Machine Learning?**

- Supervised algorithms-

Supervised learning is the type of learning in which input & output is known, & you write an algorithm to learn the mapping process or relation between them.

Most algorithms are based on supervised learning.

- Unsupervised algorithms-

In unsupervised learning, the output is unknown & the algorithms must be written in a way that makes them self-sufficient in determining the structure & distribution of data.

**Prerequisites**

Computer science students & other students with an engineering background find it easier to learn Machine learning. However, anyone with good or at least a basic knowledge in the following domains can master the subject at beginner level: –

- Fundamentals of programming-

Fundamentals of programming include a good grip of basic programming, data structures & its algorithms.

- Probability & statistics-

Key probability topics like axioms & rules, Baye's theorem, regression etc. must be known.

Knowledge on statistical topics like mean, median, mode, variance, & distributions like normal, Poisson, binomial etc. is required.

- Linear Algebra-

Linear algebra is the representation of linear expressions in the form of matrices & vector spaces. For this, one must be well informed about topics like matrices, complex numbers & polynomial equations.

NOTE: These prerequisites are for beginners.

**Job prospects in Machine learning** à

Owing to its limitless applications & use in modern & improvised technology, demand for its professionals is increasing day by day, & it would never ever go out of trend.

A professional can find jobs in the following fields: –

- Machine learning engineer
- Data engineer
- Data analyst
- Data scientist