Machine Learning with Naïve Bayes

with Hristina Hristova
4.8/5
(735)

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

2 hours of content 5232 students

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 2 hours of content
  • 14 Interactive exercises
  • 12 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Machine Learning with Naïve Bayes

A course by Hristina Hristova

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 2 hours of content
  • 14 Interactive exercises
  • 12 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

$99.00

Lifetime access

Buy now

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 2 hours of content
  • 14 Interactive exercises
  • 12 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • Master foundational machine learning techniques with Naïve Bayes that will take your data analysis skills to the next level
  • Fully grasp the Bayes theorem and its practical application
  • Understand the pros and cons of the Naïve Bayes algorithm to make informed decisions in model selection
  • Build and optimize classification models with scikit-learn
  • Integrate essential math concepts with hands-on Python programming skills
  • Be able to independently manage and execute a complete ML project from start to finish

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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.

Learn for Free

What does the course cover?

1.1 What does the course cover?

4 min

Motivation

1.2 Motivation

4 min

Bayes' thought experiment

1.3 Bayes' thought experiment

3 min

Assignment 1

1.5 Assignment 1

1 min

Bayes' theorem

1.6 Bayes' theorem

7 min

Assignment 2

1.8 Assignment 2

1 min

Interactive Exercises

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

categorize exercise
true-false exercise
single-choice exercise
multiple-choice exercise
flashcards exercise
fill-blank-typing exercise
fill-blank-selection exercise
order exercise

Curriculum

  • 1. Bayes' Theorem
    7 Lessons 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?
    4 min
    Motivation
    4 min
    Bayes' thought experiment
    3 min
    Assignment 1 Read now
    1 min
    Bayes' theorem
    7 min
    Assignment 2 Read now
    1 min
    The ham-or-spam example
    7 min
  • 2. Setting up the Environment
    2 Lessons 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 Read now
    1 min
    Installing the relevant packages
    4 min
  • 3. Naïve Bayes Algorithm - Practical Example
    7 Lessons 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
    6 min
    CountVectorizer
    6 min
    The YouTube Dataset: Preprocessing
    7 min
    The YouTube Dataset: Classification
    4 min
    The YouTube Dataset: Confusion matrix
    4 min
    The YouTube Dataset: Accuracy, Precision, Recall, and the F1 score
    7 min
    The YouTube Dataset: Changing the priors
    6 min

Topics

TheoryPythonMathematicsMachine LearningProgrammingMachine and Deep Learning

Tools & Technologies

python

Course Requirements

  • Basic skills in statistics and probability are required
  • You need to complete an introduction to Python before taking this course
  • It is highly recommended to take the Machine Learning in Python course first
  • You need to have Jupyter Notebook up and running

Who Should Take This Course?

Level of difficulty: Intermediate

  • Aspiring data scientists and ML engineers

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Hristina Hristova

Hristina Hristova

Head of Data Content at

6 Courses

1665 Reviews

14092 Students

Hristina Hristova is a Theoretical Physicist with experience in the fields of mathematics, physics, programming, and the creation of various educational content. For several years now, she has been tutoring physics and mathematics students online, following educational programs such as The IB Diploma, Cambridge IGCSE, and Cambridge AS & A Level, among many others. Hristina’s high qualification and adaptive teaching style have helped plenty of students successfully pass their exams, while also enjoying the learning process.

What Our Learners Say

Yaw Agyare
Yaw Agyare
31.03.2025
It's Great!!
Sophia Miranda
Sophia Miranda
27.03.2025
Amir Lanwer
Amir Lanwer
26.03.2025

365 Data Science Is Featured at

Our top-rated courses are trusted by business worldwide.

Recommended Courses

SQL + Tableau + Python

SQL + Tableau + Python

with Martin Ganchev
4.8/5
(458)
5 hours of content
Product Management for AI & Data Science

Product Management for AI & Data Science

with Danielle Thé
4.9/5
(707)
6 hours of content
Deep Learning with TensorFlow 2

Deep Learning with TensorFlow 2

with Iskren Vankov, Iliya Valchanov
4.8/5
(942)
5 hours of content
Customer Analytics in Python

Customer Analytics in Python

with Nikolay Georgiev, Elitsa Kaloyanova
4.9/5
(384)
10 hours of content
Machine Learning in Python

Machine Learning in Python

with Iliya Valchanov
4.8/5
(1,551)
7 hours of content
Convolutional Neural Networks with TensorFlow in Python

Convolutional Neural Networks with TensorFlow in Python

with Nikola Pulev, Iskren Vankov
4.8/5
(520)
4 hours of content
Data Literacy

Data Literacy

with Olivier Maugain
4.8/5
(1,935)
5 hours of content
AI Applications for Business Success

AI Applications for Business Success

with Horia Mărgărit
4.7/5
(365)
2 hours of content
Linear Algebra and Feature Selection

Linear Algebra and Feature Selection

with Aleksandar Samsiev, Ivan Manov
4.7/5
(366)
3 hours of content
Machine Learning in Excel

Machine Learning in Excel

with Ivan Kitov
4.9/5
(547)
7 hours of content
Machine Learning with Support Vector Machines

Machine Learning with Support Vector Machines

with Elitsa Kaloyanova
4.8/5
(590)
1 hour of content
Machine Learning with Decision Trees and Random Forests

Machine Learning with Decision Trees and Random Forests

with Nikola Pulev
4.8/5
(664)
1 hour of content
Machine Learning with K-Nearest Neighbors

Machine Learning with K-Nearest Neighbors

with Hristina Hristova
4.8/5
(624)
1 hour of content
Machine Learning with Ridge and Lasso Regression

Machine Learning with Ridge and Lasso Regression

with Ivan Manov
4.8/5
(347)
1 hour of content
The Machine Learning Process A-Z

The Machine Learning Process A-Z

with Ken Jee, Jeff Li
4.8/5
(976)
6 hours of content
The Machine Learning Algorithms A-Z

The Machine Learning Algorithms A-Z

with Jeff Li, Ken Jee
4.7/5
(608)
5 hours of content
Machine Learning Deep Dive: Business Applications and Coding Walkthroughs

Machine Learning Deep Dive: Business Applications and Coding Walkthroughs

with Jeff Li, Ken Jee
4.8/5
(229)
3 hours of content
ChatGPT for Data Science

ChatGPT for Data Science

with Elitsa Kaloyanova
4.8/5
(243)
2 hours of content
Intro to AI

Intro to AI

with Ned Krastev
4.8/5
(707)
2 hours of content