Machine learning is a subfield of artificial intelligence (AI) that enables computers to develop and adapt on their own without being programmed.
The learning process starts with observations or data, such as examples, direct experience, or instruction, so that we can seek patterns in data and make better decisions in the future based on the examples we provide. The fundamental goal is for computers to learn on their own, without the need for human involvement, and to change their behavior accordingly.
However, the text is treated as a series of keywords when using traditional machine learning algorithms; instead, a semantic analysis technique mimics the human ability to comprehend the meaning of a document.
To use categorical data for machine classification, the text labels must be encoded into a different format. There are two widely used encodings.
The first is label encoding, which replaces each text label value with a number. The other method is one-hot encoding, which converts each text label value into a binary value column.
The majority of machine learning frameworks include functions that perform the conversion for you. Label encoding can occasionally fool the machine learning system into thinking the encoded column is ordered, hence one-hot encoding is preferred.
You must normalize numeric data before using it for machine regression.
Otherwise, larger range numbers may tend to emphasize the Euclidean distance between feature vectors, and they may be amplified at the expense of the other fields, and the gradient descent optimization might have a hard time merging.
A “feature” is similar to an explanatory variable, which is used in statistical techniques such as linear regression. Feature vectors are numerical vectors that combine all of the features for a single row.
Choosing a minimum set of independent variables that explain the problem is part of the art of feature selection.
If two variables are highly correlated, they should either be combined into a single feature or one of them should be dropped.
Some of the transformations used to create new features or reduce the dimensionality of feature vectors are straightforward.
Machine learning algorithms range in complexity from logistic and linear regression to combinations of other models (called ensembles) and to deep neural networks.
So how do Machine Learning algorithms learn?
Machine learning employs two techniques: supervised learning, which involves training a model on known output and input data. The second technique is unsupervised learning that involves discovering elements in input data such as interior structures and hidden patterns.
So how should we define what is machine learning algorithm? They are the engines of machine learning and they convert a data set into a model.
The type of algorithm that works best (unsupervised, supervised, classification, regression) is determined by the type of problem being solved, the computing resources available, and the nature of the data.