Ans. Machine Learning:
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior.
Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
Example
Google Translate was possible because it “trained” on the vast amount of information on the web, in different languages.
Perspectives
- Machine learning is a subfield of artificial intelligence and machine learning algorithms are used in other related fields like natural language processing and computer vision.
- In general, there are three types of learning and these are supervised learning, unsupervised learning, and reinforcement learning.
- Their names tell the main idea behind them actually.
- In supervised learning, your system learns under the supervision of the data outputs so supervised algorithms are preferred if your dataset contains output information.
Issues
Lack of quality of data
- One of the main issues in Machine Learning is the absence of good data.
- While, algorithms tend to make developers exhaust most of their time on artificial intelligence.
Fault in credit card fraud detection
- Although this AI-driven software helps to successfully detect credit card fraud, there are issues in Machine Learning that make the process redundant.
Getting bad recommendations
- Proposal engines are quite regular today.
- While some might be dependable, others may not appear to provide the necessary results.
Talent deficit
- Albeit numerous individuals are pulled into the ML business, however, there are still not many experts who can take complete control of this innovation.
Making the wrong assumptions
- ML models can’t manage datasets containing missing data points.
- Thus, highlights that contain a huge part of missing data should be erased.
References/Sources:
- https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained