The Future of Machine Learning
Photo by Sergey Zolkin on Unsplash
Machine Learning is a highly demanding concept in today's world where there has been a massive transitioning to automation of tasks and remote working facilities.
Advancing through the phases of technological changes, we have now mastered cloud technologies which serves as a vital component of AI/ML. Storing numerous amounts of untrained data and processing them to enable computers to mimic human behavior.
The Global Automated Machine Learning Market (henceforth referred to as the market studied) was valued at USD 665. 63 Million in 2021, and it is expected to reach USD 5,406. 75 Million by 2027, registering a CAGR of 42.
Artificial neural networks have the uniqueness to enable machine thinking ability to solve complex problems.
The quantum computers have proven to solve certain problems even faster than our present classical computers. Adding an advantage to machine learning, quantum neural networks enhances a model to fit more functions than the latter.
The main technical contribution of our work motivates a new way to capture the capacity of any model (classical or quantum) with a measure called the effective dimension.
However, in machine learning, there exists some data leakage causing accidental sharing of information between the test and the training data sets. An existence of a feature where an outcome of a future happening gets included within the current situations will cause the problem of data leakage.
A prediction to a model is solely dependant on the variability of occurences in the current state. Thus quantum computing enables models with little redundancy to ensure we are getting the most out of it. Here the highly coorelated attributes can lead to a better determination of dropping the redundant attribute.