Last Updated on March 8, 2022 by Rajeev Bagra
Source: 365 Data Science newsletter dated 7 March 2022
At 365 Data Science, we share the same mission – to provide high-quality, accessible online education for everyone, regardless of their background or experience.
We have helped over 1.5M students (and counting!) around the world fulfill their learning goals and start a successful career in data science. And one of the ways we’ve achieved that is by continuously improving our curriculum with new, in-demand courses at no additional cost.
So, today I’m excited to announce the latest addition to our program: Machine Learning with Naive Bayes.
Why Machine Learning with Naïve Bayes?
The Naïve Bayes classifier has certain advantages compared to other machine learning algorithms or complex neural networks, the biggest ones being that it’s fast and handles sparse data quite impressively. The classifier shines in the fields of text analysis and text mining, as well as natural language processing. If you have ever wondered how big social media platforms flag and filter out harmful comments, one way to perform such tasks is with the help of Naïve Bayes. In fact, that’s exactly the practical use case you’ll find in this course!
Who is this course for?
This course is perfect for anyone who wants to level up their machine learning skillset, learn how to come up with out-of-the-box solutions, and expand their career opportunities in the data science field. By introducing you to a rather simple, yet quite powerful algorithm, this course will help you become a better programmer and efficient problem-solver who understands that sometimes simpler is better.
What will you learn in this course?
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.
Under her guidance, you will learn:
- The components of Bayes’ theorem
- How to apply Bayes’ theorem
- What “naïve” is in Naïve Bayes
- How to use scikit-learn’s Multinomial Naïve Bayes
- Pros and cons of the Naïve Bayes algorithm
- Applications of the Naïve Bayes algorithm
Of course, you can always count on expert support from your instructor. She is expecting you in the 365 Q&A Hub and is ready to answer all your questions.
365 Data Science