Natural Language Processing, or NLP as it is commonly known, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable. It refers to analytics tasks that deal with natural human language, in the form of text or speech.
Gradient boost is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.
Random forests are an ensemble learning method for classification and regression that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the individual trees.
Logistic regression is a classification algorithm used on a discrete set of classes. Logistic regression transforms its output using the logistic sigmoid function to return a probability value which can then be mapped to two or more discrete classes. Logistic regression is an extension of simple linear regression.
Networking is very important in today’s world specially where we all are getting used to the ‘New Normal’ of online meetings and greetings. Networking is not just exchanging information about each other but it is also a means to build long lasting relationships. Human beings are ‘Social Animals’ and this is for a reason. ‘I’ cannot survive alone. ‘I’ need ‘We’ to survive not just personally but professionally too and for this reason Networking plays a huge role.