Machine Learning Tutorial Python - 14: Naive Bayes Classifier Algorithm Part 1

This is part 1 of naive bayes classifier algorithm machine learning tutorial. Naive bayes theorm uses bayes theorm for conditional probability with a naive assumption that the features are not correlated to each other and tries to find conditional probability of target variable given the probabilities of features. We will use titanic survival dataset here and using naive bayes classifier find out the survival probability of titanic travellers. We use sklearn library and python for this beginners machine learning tutorial. GaussianNB is the classifier we use to train our model. There are other classifiers such as MultinomialNB but we will use that in part 2 of the tutorial. #MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #NaiveBayes #sklearntutorials #scikitlearntutorials Code: https://github.com/codebasics/py/blob/master/ML/14_naive_bayes/14_naive_bayes_1_titanic_survival_prediction.ipynb Naive bayes theory video: https://www.youtube.com/watch?v=Q8l0Vip5YUw Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses. Exercise solution: https://github.com/codebasics/py/blob/master/ML/14_naive_bayes/Exercise/14_naive_bayes_exercise.ipynb Topics that are covered in this Video: 00:00 introduction 00:19 Basics of probability 00:52 Conditional probability 01:52 Bayes theorm 04:37 Coding: titanic crash survival 10:00 GaussianNB classifier Next Video: Machine Learning Tutorial Python - 15: Naive Bayes Part 2: https://www.youtube.com/watch?v=nHIUYwN-5rM&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=16 Populor Playlist: Data Science Full Course: https://www.youtube.com/playlist?list=PLeo1K3hjS3us_ELKYSj_Fth2tIEkdKXvV Data Science Project: https://www.youtube.com/watch?v=rdfbcdP75KI&list=PLeo1K3hjS3uu7clOTtwsp94PcHbzqpAdg Machine learning tutorials: https://www.yo