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: [ Ссылка ]
Naive bayes theory video: [ Ссылка ]
Do you want to learn technology from me? Check [ Ссылка ] for my affordable video courses.
Exercise solution: [ Ссылка ]
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: [ Ссылка ]
Populor Playlist:
Data Science Full Course: [ Ссылка ]
Data Science Project: [ Ссылка ]
Machine learning tutorials: [ Ссылка ]
Pandas: [ Ссылка ]
matplotlib: [ Ссылка ]
Python: [ Ссылка ]
Jupyter Notebook: [ Ссылка ]
Tools and Libraries:
Scikit learn tutorials
Sklearn tutorials
Machine learning with scikit learn tutorials
Machine learning with sklearn tutorials
🌎 My Website For Video Courses: [ Ссылка ]
Need help building software or data analytics and AI solutions? My company [ Ссылка ] can help. Click on the Contact button on that website.
#️⃣ Social Media #️⃣
🔗 Discord: [ Ссылка ]
📸 Dhaval's Personal Instagram: [ Ссылка ]
📸 Codebasics Instagram: [ Ссылка ]
🔊 Facebook: [ Ссылка ]
📱 Twitter: [ Ссылка ]
📝 Linkedin (Personal): [ Ссылка ]
📝 Linkedin (Codebasics): [ Ссылка ]
🔗 Patreon: [ Ссылка ]
