Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn

This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what the Naive Bayes algorithm is and Bayes theorem in general. You will understand conditional probability concepts, where the Naive Bayes classifier is used and how the Naive Bayes algorithm works. By the end of this video, you will also implement the Naive Bayes algorithm for text classification in Python.
🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=Description&utm_source=youtube

Dataset Link - https://drive.google.com/drive/folders/1yqGMb98BG2rdP2CP8o6dipuZBt7Hexfd

The topics covered in this Naive Bayes video are as follows:
00:00 - 01:06 Introduction and Agenda
01:06 - 05:45 What is Naive Bayes?
05:45 - 06:30 Why do we need Naive Bayes?
06:30 - 20:17 Understanding Naive Bayes Classifier
20:17 - 22:36 Advantages of Naive Bayes Classifier
22:36 - 43:45 Demo - Text Classification using Naive Bayes

✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

⏩ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4

For a more detailed understanding on Naive Bayes Classifier, do visit: https://bit.ly/2DHxctD

You can also go through the Slides here: https://goo.gl/Cw9wqy

#NaiveBayesClassifer #NaiveBayes #NaiveBayesAlgorithm #NaiveBayesInMachineLearning #NaiveBayesMachineLearning #NaiveBayesClassiferExample #MachineLearningAlgoithms #MachineLearning #Simplilearn

What is Naive Bayes Classifier?
Naive Bayes is a supervised learning algorithm that is based on applying Bayes’ theorem with the “naive” assumption. The Bayes Rule gives the formula for the probability of Y given X. It is called Naive because of the naive assumption that the X’s are independent of each other.

Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer

Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:

1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

The Machine Learning Course is recommended for:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning

👉Learn more at: https://bit.ly/3fouyY0

For more information about Simplilearn’s courses, visit:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn
- Website: https://www.simplilearn.com

Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Видео Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn канала Simplilearn