Machine Learning with Naïve Bayes

with Hristina Hristova
4.8/5
(624)

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

2 hours of content 4335 students
Limited-Time Offer
20% off

$99.00

$79.00

Lifetime access

00 Days
:
12 Hours
:
51 Minutes
:
57 Seconds
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14-Day Money-Back Guarantee

What you get:

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

Machine Learning with Naïve Bayes

Limited-Time Offer
20% off

$99.00

$79.00

Lifetime access

00 Days
:
12 Hours
:
51 Minutes
:
57 Seconds
Get 20% OFF
14-Day Money-Back Guarantee

What you get:

  • 2 hours of content
  • 12 Downloadable resources
  • Interactive exercises
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement
Limited-Time Offer
20% off

$99.00

$79.00

Lifetime access

00 Days
:
12 Hours
:
51 Minutes
:
57 Seconds
Get 20% OFF
14-Day Money-Back Guarantee

What you get:

  • 2 hours of content
  • 12 Downloadable resources
  • Interactive exercises
  • 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.

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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

1.0 Assignment 1

1 min

Bayes' theorem

1.4 Bayes' theorem

7 min

The ham-or-spam example

1.5 The ham-or-spam example

7 min

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
    The ham-or-spam example
    7 min
    Assignment 2 Read now
    1 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 learningProgramming

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 365 Data Science

4 Courses

1217 Reviews

9198 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

03.10.2024
Yeah i like the tutor aaand it's short
03.10.2024
26.09.2024

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