Online Course
Machine Learning with Naïve Bayes

Name: Machine Learning with Naïve Bayes Course
Price: 36 USD

Master machine learning with Naïve Bayes: learn the theoretical foundations behind the Bayesian approach and gain practical problem-solving skills

4.9

870 reviews on

Start for free

5,856 students already enrolled

Skill level:

Intermediate

Duration:

2 hours

Lessons (1 hour)

Practice exams (30 minutes)

CPE credits:

2

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Master machine learning techniques with Naïve Bayes.
Fully grasp Bayes’ theorem and its practical applications.
Understand the pros and cons of the Naïve Bayes algorithm.
Build and optimize classification models with scikit-learn.
Manage and execute a complete ML project from start to finish.

Topics & tools

TheoryPythonMathematicsMachine LearningProgrammingMachine and Deep Learning

Your instructor

Hristina Hristova

Head of Data Content at 365 Data Science | Theoretical Physicist | Educator in Physics, Mathematics, and Programming

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Theoretical and Mathematical Physics

Ask Hristina a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 2 Field of Study: Information Technology
Delivery Method: QAS Self Study

Knowledge on various machine learning algorithms is essential for machine learning enthusiasts and specialists. This course focuses on a specific type of classifier – the Naïve Bayes one. It is famous for being a quick learner and a real-time problem solver. Not only will you learn the theoretical foundations behind the Bayesian approach, but you will also get the chance to solve a real-life problem using scikit-learn’s Naïve Bayes algorithms.

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Prerequisites

Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
Intermediate Python skills are required.
Familiarity with basic statistics and linear algebra is helpful but not mandatory.

Advanced preparation

Curriculum

16 lessons 14 exercises 2 exams

1. Bayes' Theorem

27 min

This section serves as a theoretical introduction to the Bayesian approach which will later help us understand the Naïve Bayes machine learning classification algorithm. We start with an intuitive example which Thomas Bayes himself introduced. Then, we dive into the mathematics behind his approach and derive Bayes’ theorem. Finally, we apply this theorem to classify an e-mail message as a spam or not-spam (also known as a ham).

27 min

This section serves as a theoretical introduction to the Bayesian approach which will later help us understand the Naïve Bayes machine learning classification algorithm. We start with an intuitive example which Thomas Bayes himself introduced. Then, we dive into the mathematics behind his approach and derive Bayes’ theorem. Finally, we apply this theorem to classify an e-mail message as a spam or not-spam (also known as a ham).

What does the course cover? Free

Motivation Free

Bayes' thought experiment Free

Exercise Free

Assignment 1 Free

Bayes' theorem Free

Exercise Free

Assignment 2 Free

The ham-or-spam example Free

Exercise Free
2. Setting up the Environment

5 min

In this section you will learn how to install all Python packages relevant for the next part of the course focused on practice.

5 min

In this section you will learn how to install all Python packages relevant for the next part of the course focused on practice.

Setting up the environment Free

Installing the relevant packages Free
3. Naïve Bayes Algorithm - Practical Example

40 min

This is the practical section of the course where we roll our sleeves up and build our very own classification model. We use a dataset containing YouTube comments – some well-intended and others harmful. Our task is to train a model that could later serve as a spam comment detector. To do this, we make use of Python’s scikit-learn library, where a Naïve Bayes algorithm is implemented. Throughout this section, we will study the inner workings of the algorithm and learn how to interpret performance metrics such as accuracy, precision, recall, and F1 score.

40 min

This is the practical section of the course where we roll our sleeves up and build our very own classification model. We use a dataset containing YouTube comments – some well-intended and others harmful. Our task is to train a model that could later serve as a spam comment detector. To do this, we make use of Python’s scikit-learn library, where a Naïve Bayes algorithm is implemented. Throughout this section, we will study the inner workings of the algorithm and learn how to interpret performance metrics such as accuracy, precision, recall, and F1 score.

The YouTube Dataset: Creating the data frame

CountVectorizer

The YouTube Dataset: Preprocessing

Exercise

The YouTube Dataset: Classification

Exercise

The YouTube Dataset: Confusion matrix

Exercise

The YouTube Dataset: Accuracy, Precision, Recall, and the F1 score

Exercise

The YouTube Dataset: Changing the priors

Practice exam
4. Course exam

35 min

35 min

Course exam

Free lessons

1.1 What does the course cover?

4 min

1.2 Motivation

4 min

1.3 Bayes' thought experiment

3 min

1.5 Assignment 1

1 min

1.6 Bayes' theorem

7 min

1.8 Assignment 2

1 min

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