Perspectives and Issues in Machine Learning
One useful perspective on machine learning is that involves searching a very large space of possible hypothesis to determine one that best fits the observed data and any prior knowledge held by the learner.
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.
Example: Let’s assume you have a medical statistic company and you have a dataset which contains patients’ features like blood pressure, sugar rate in their blood, heart rate per minute, etc.
Lack Of Quality 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
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.
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.
Our checkers example raises a number of generic questions about machine learning. The field of machine learning is concerned with answering questions such as the following:
What algorithms exist for learning general target functions from specific training
examples? In what settings will particular algorithms converge to the desired function,
given sufficient training data? Which algorithms perform best for which types of
problems and representations?
How much training data is sufficient? What general bounds can be found to relate the confidence in learned hypotheses to the amount of training experience and the character of the learner's hypothesis space?
When and how can prior knowledge held by the learner guide the process of generalizing from examples? Can prior knowledge be helpful even when it is only approximately correct?
What is the best strategy for choosing a useful next training experience, and how does the choice of this strategy alter the complexity of the learning problem?
What is the best way to reduce the learning task to one or more function approximation problems? Put another way, what specific functions should the system attempt to learn? Can this process itself be automated?
How can the learner automatically alter its representation to improve its ability to represent and learn the target function?