Supervised Machine Learning with Logistic Regression and Naïve Bayes
Learn supervised machine learning with logistic regression and Naïve Bayes. Enroll in our free course to gain essential skills and knowledge to take your understanding of these algorithms to the next level.
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About this Course
In this course, we will cover the fundamentals of supervised machine learning and dive deeper into two popular algorithms: logistic regression and Naïve Bayes.
We will start with an overview of supervised learning and explore the key concepts and terminology used in this area of machine learning. From there, we will delve into logistic regression and learn how to apply this algorithm to binary classification problems. We will also cover evaluation metrics and techniques to improve model performance. Next, we will move on to Naïve Bayes, a probabilistic classifier that is widely used in text classification and spam filtering. We will learn how to implement Naïve Bayes and how to interpret the results.
Throughout the course, we will provide practical examples and case studies to help you understand the real-world applications of these algorithms. By the end of this course, you will have a solid understanding of supervised machine learning with logistic regression and Naïve Bayes, and the skills to apply them to your own projects. So, what are you waiting for? Enroll now and start your learning journey today!
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Course Outline
This module begins by defining machine learning. It then discusses how a machine understands the tasks with examples and explains supervised and unsupervised learning concepts in machine learning.
This module explains what is Supervised Learning and two types of Supervised Learning that is Regression and Classification.
This module highlights Logistic Regresion which is also a part of Supervised Learning Classification Algorithm.
This module showcases a demo on logistic regression with a real world example of Credit Card Fraud.
This module elaborates on Naive Bayes classification which is a part of Supervised Learning.
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