My name is Hristina and I am happy to introduce you to my first contribution to the 365 Data Science Program – Machine Learning with Naïve Bayes!
Teaching and machine learning are two of my greatest passions and I’m very excited that I had a chance to combine them in this bite-sized practical course.
In this post, I will share details about the topics covered, the hands-on skills it will help you build, as well as a little more about my educational background and professional experience before joining 365 Data Science.
The 365 Data Science Machine Learning with Naïve Bayes Course
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?
The Machine Learning with Naïve Bayes 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?
Under my guidance, you will learn:
- the components of Bayes’ theorem
- how to apply Bayes’ theorem
- what “naïve” in Naïve Bayes is
- how to use scikit-learn’s Multinomial Naïve Bayes
- the pros and cons of the Naïve Bayes algorithm
- the applications of the Naïve Bayes algorithm
About the Author
I’m a Theoretical Physicist with a background in mathematics, physics, and programming. Prior to pursuing a degree in Physics at Lund University in Sweden, I had studied Information Technology in Denmark. I have also been creating educational content for years, mentoring and teaching physics and mathematics students online. Some of the educational programs I have been working with include The IB Diploma, Cambridge IGCSE, and Cambridge AS & A Level, among others. I am happy that my qualifications, experience, and teaching style have helped many students prepare to successfully pass their exams, while also having fun during the process.
Ready to Learn Machine Learning with Naïve Bayes?
Visit the Machine Learning with Naïve Bayes course page to find more details about its curriculum or sign up below to try it out for free.