Watch this tutorial on Logistic Regression and Naive Bayes in Machine Learning. Logistic Regression is a statistical method to model the probability of existing events and is used for the purpose of classification. Naive Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data. Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class
Great Learning brings you this tutorial on Logistic Regression and Naive Bayes in Machine Learning to help you understand this topic and getting started on the journey to learn about it well. This video starts by Understanding the need of Machine Learning in problem Solving, followed by understanding the topic of Logical Regression & Naive Bayes classification. Then we look at the concept of the Bayes Theorem. Finally, we look at Diabetes prediction using Naive Bayes. This video teaches Logistic Regression and Naive Bayes in Machine Learning with an in-depth demonstration & examples to help you get started on the right foot.
- 0:00:00 Introduction
- 0:01:11 Agenda
- 0:02:14 Why do we need Machine Learning?
- 0:06:58 What is Machine Learning?
- 0:07:56 Traditional programming vs Machine Learning
- 0:09:07 Machine Learning lifecycle
- 0:13:04 Types of Machine Learning
- 0:14:10 What is supervised learning?
- 0:21:20 What is Logistic Regression?
- 0:30:10 Credit card fraud detection demo
- 0:57:57 Introduction Naive Bayes classification
- 1:00:55 Example of Bayes Theorem
- 1:07:08 Diabetes prediction using Naive Bayes
- 1:32:53 Summary
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