Machine Learning with Naïve Bayes
Master machine learning with Naïve Bayes: learn the theoretical foundations behind the Bayesian approach and gain practical problem-solving skills
$99.00
Lifetime access
What you get:
- 2 hours of content
- 3 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
$99.00
Lifetime access
What you get:
- 2 hours of content
- 3 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
$99.00
Lifetime access
What you get:
- 2 hours of content
- 3 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
1.1 What does the course cover?
4 min
1.2 Motivation
4 min
1.3 Bayes' thought experiment
3 min
1.0 Assignment 1
1 min
1.4 Bayes' theorem
7 min
1.5 The ham-or-spam example
7 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.
Curriculum
- 2. Setting up the Environment2 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 now1 minInstalling the relevant packages4 min - 3. Naïve Bayes Algorithm - Practical Example7 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 frame6 minCountVectorizer6 minThe YouTube Dataset: Preprocessing7 minThe YouTube Dataset: Classification4 minThe YouTube Dataset: Confusion matrix4 minThe YouTube Dataset: Accuracy, Precision, Recall, and the F1 score7 minThe YouTube Dataset: Changing the priors6 min
Topics
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.
Meet Your Instructor
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.
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