Machine learning & the need for it à
Machine learning is a subfield of AI, in which a computer system is fed with algorithms designed to analyse and interpret different types of data on their own. These learning algorithms gain the ability to analyze when they are trained to do so using sample data.
It is useful when the volume of data to be analysed is very large & outside human limits. It can be used to draw important conclusions & make important decisions.
Some important areas where it applies:
- Cancer treatment.
Chemotherapy, which is used to kill cancer cells, carries the risk of killing even 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 procedures can be performed in parts of the human body where spaces are narrow &- the risk of the doctor messing up the procedure is high. Robotic surgery is trained using machine learning algorithms.
- Finance-
It is used to detect fraudulent banking transactions within seconds that would take a human being hours to notice.
The utility of machine learning is endless &- can be used in many fields.
What does one learn in machine learning?
- Supervised algorithms;
Supervised learning is the type of learning in which the input & output are known, & you write an algorithm to learn the matching process or the relationship between them.
Most algorithms are based on supervised learning.
- Unsupervised algorithms -
In unsupervised learning, the output is unknown & algorithms must be written in a way that makes them self-sufficient in determining the structure & distribution of the data.
Prerequisites
Computer science students & other students with engineering backgrounds find it easier to learn machine learning. However, anyone with good or at least basic knowledge in the following areas can master the subject at a beginner level.
- Basic programming principles.
The fundamentals of programming include a good knowledge of basic programming, data structures & its algorithms.
- Probabilities & statistics...
You should be familiar with basic probability topics such as axioms and rules, Baye's theorem, regression, etc.
Knowledge of statistical topics such as mean, median, mode, variance, &? Knowledge of distributions such as normal, Poisson, binomial, etc.
- Linear algebra-
Linear algebra is the representation of linear expressions in the form of matrices and vector spaces. For this purpose, one needs to be well versed on topics such as matrices, complex numbers & polynomial equations.
NOTE: These prerequisites are for beginners.
Job prospects in machine learning à
Due to its unlimited applications & its use in modern & self-designed technology, the demand for its professionals is increasing day by day, & will never go out of trend.
A professional can find work in the following areas: -
- Machine learning engineer
- Data Engineer
- Data Analyst
- Data scientist
