Naive Bayes Theorem | Introduction to Naive Bayes Theorem | Machine Learning Classification
Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. It is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles. It is not only known for its simplicity, but also for its effectiveness. It is fast to build models and make predictions with Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving text classification problem. Hence, you should learn this algorithm thoroughly.
This video will talk about below:
1. Machine Learning Classification
2. Naive Bayes Theorem
About us: HackerEarth is building the largest hub of programmers to help them practice and improve their programming skills.
At HackerEarth, programmers:
1. Solve problems on Algorithms, DS, ML etc(https://goo.gl/6G4NjT).
2. Participate in coding contests(https://goo.gl/plOmbn)
3. Participate in hackathons(https://goo.gl/btD3D2)
Subscribe Our Channel For More Updates : https://goo.gl/suzeTB
For More Updates, Please follow us on:
Facebook : https://goo.gl/40iEqB
Twitter : https://goo.gl/LcTAsM
LinkedIn : https://goo.gl/iQCgJh
Blog : https://goo.gl/9yOzvG