In the sixth Machine Learning tutorial I explain what Bayes Theorem is, how the Naive Bayes Classifier works, I give a Maximum Likelihoods calculation example and a step by step walk-through of a simple Multinominal Naive Bayes problem. After this Machine Learning example video, you should be able to understand how Bayes Theorem works and how to use the Naive Bayes Classifier to solve big-data classification problems.
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Bayes Theorem is a mathematical approach based on probability, which can be used as a Machine Learning algorithm in the from of the Naive Bayes Classifier.
The Naive Bayes is a supervised machine learning algorithm, which allows the calculation of the probability of a pattern to be part of a particular class (posterior probability) based on the previous knowledge about the probability of this pattern to be part of the particular class (class-conditional probability) and the overall probability of the class (prior probability & evidence). The classifier makes a decision based on maximizing the posterior probability.
The Naive Bayes is extended into Multinominal Naive through the use of maximum-likelihoods, which allow us to calculate the posterior probability of a pattern containing multiple features.
The Naive Bayes classifier is simple but extremely efficient & powerful machine learning algorithm thanks to its robustness. It is called Naive Bayes because it assumes the data samples are independent & normally distributed but even when these rules a somewhat broken, the Naive Bayes still works very well.
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I explain what is Naive Bayes, what maximum likelihoods are, how to solve a Multinominal Naive Bayes problem, what is class-conditional probability, how to calculate posterior probability, & prior probability.
I explain simply what Machine Learning is and how simple Artificial Intelligence systems work. This is part of my series on Machine Learning Tutorials, where we will explore the world of A.I. together and learn how to create A.I.! I hope you found it interesting, SUBSCRIBE to stay tuned for more!