Machine Learning with Naïve Bayes
Naïve Bayes Classifier is a supervised classification machine learning algorithm inspired by the Bayes Theorem. Its ability to make intuitive real time-predictions from small non-linear sets makes it perfect for consumer behavior predictions, recommendation systems and text analysis - news article categorization, email category filtering and sentiment analyses. In the free Machine Learning with Naïve Bayes pdf course notes we are going to build upon your sklearn Naïve Bayes skills by going over the algorithm’s computational capabilities, outlining the 7 steps in creating a supervised machine learning model and identifying 6 relevant metrics to use for performance evaluation.
Who is it for
This Machine Learning with Naïve Bayes study guide is meant for aspiring Data Scientists, Machine Learning Engineers, Business Analysts, Data Engineers, Programmers and anyone who is looking to leverage the predictive analytical capabilities of machine learning to create automated models that are at the forefront of business growth.
How it can help you
As data increases in size and complexity, the need for advanced computational capabilities is increasing proportionally. The Naïve Bayes classifier lecture notes will provide you with the necessary skills to build a machine learning algorithm that solves non-linear problems, make present time predictions, and utilize the algorithm’s classification abilities.