I should first clarify what machine learning is. Additional fundamental queries include:
Do machines actually learn?
How do they learn, if at all?
Which issue can be regarded as a properly framed learning issue?
What crucial characteristics are needed to accurately describe a learning problem?
It is crucial to formalise the definition of machine learning at the outset.
This will answer the first query, which is whether machines actually learn. Machine learning can be defined in a variety of ways. But Tom M. Mitchell's assertion, a professor in the School of Computer Science at Carnegie Mellon University's Machine Learning Department, is likely the most pertinent, succinct, and widely acknowledged. Tom M. Mitchell has defined machine learning as
'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.'
This basically means that a machine can be said to learn if it can gain experience through performing a certain activity and enhance its performance in carrying out related activities in the future. When we discuss past experience, we refer to prior information that is relevant to the task. The machine receives this data as input from a source.
When discussing the process of learning to play checkers, E stands for the experience of playing the game, T for the activity of playing checkers, and P for the performance metric represented by the player's win %. For any other machine learning issue, such as an issue with image categorization, the same mapping can be used. In the context of image classification, E stands for historical data with images that have labels or classes assigned to them (for instance, whether the image is of a class cat, dog, or elephant, etc.), T for the task of classifying new, unlabelled images, and P for the performance indicator represented by the proportion of correctly classified images. Identifying your challenge is the first stage in every project. If the wrong problem is solved, even the most sophisticated algorithm will produce useless results.
Evolution of Machine Learning
Types of Machine Learning
Difference between Supervised, Unsupervised, and Reinforcement Learning
Machine Learning Activities
Types of Data in Machine Learning
Basics of Feature Engineering
Classification
Classification Learning Steps
k-Nearest Neighbour (kNN)