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Supervised Machine Learning with Logistic Regression and Naïve Bayes
4.43
Beginner level
3.0 learning hrs
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.
Instructor:
Ms. Sampriti Chatterjee
Modules updated 07/2025
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
What is Machine Learning?
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.
What is Supervised Learning?
This module explains what is Supervised Learning and two types of Supervised Learning that is Regression and Classification.
What is Logistic Regression?
This module highlights Logistic Regresion which is also a part of Supervised Learning Classification Algorithm.
Demo on Logistic Regression
This module showcases a demo on logistic regression with a real world example of Credit Card Fraud.
What is Naive Bayes?
This module elaborates on Naive Bayes classification which is a part of Supervised Learning.
Get access to the complete curriculum once you enroll in the course
4.43
What our learners enjoyed the most
Skill & tools
65% of learners found all the desired skills & tools
Our course instructor
Ms. Sampriti Chatterjee
Machine Learning Expert
Frequently Asked Questions
Will I receive a certificate upon completing this free course?
Is this course free?
What is supervised machine learning with example?
Supervised machine learning is a type of machine learning in which machines are trained using well-labeled training data and then predict the output based on that data. The labeled data indicates that some of the input data have already been tagged with the appropriate output.
Supervised learning can be applied in the real world for topics like risk assessment, image classification, fraud detection, spam filtering, and so forth.
What is difference between supervised and unsupervised machine learning?
Supervised learning is a machine learning technique that involves training models with labeled data. Models in supervised learning must discover a mapping function to connect the input variable (X) to the output variable (Y) (Y).
To train the model, supervised learning requires supervision, like how a student learns in the presence of a teacher. There are two types of problems that can be solved with supervised learning: classification and regression.
Unsupervised learning is another machine learning method that uses unlabeled input data to discover patterns. Unsupervised learning aims to extract structure and patterns from unstructured data. There is no need for monitoring when learning unsupervised. Instead, it searches the data for patterns on its own.
Why is supervised machine learning important?
Data is turned into meaningful, actionable insights with the use of supervised machine learning. It allows businesses to use data to better understand and prevent undesirable consequences while also increasing desired outcomes for their target variable.
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