I am excited to announce my latest course – Data Science & Machine Learning: Naive Bayes in Python.
Don’t want to read the spiel? Just click here to get the course:
I get it. Money is tight. Inflation is sky-high. Everything costs more. Your boss gives you a meager raise which doesn’t help much. Because of this, I’ve decided to move back to lower-cost courses. This is the first of hopefully many more to come over the next year.
For those of you who’ve been taking my courses for a long time, you must be wondering: “What the heck!? Why Naive Bayes?”
Ask yourself: Do you really know how Naive Bayes works? Well enough to implement it from scratch? During a job interview?
I’ve always wanted to go back to the fundamentals (and yes, there will be more).
Thanks to your feedback, I’ve designed a new way to deliver this content, with beginner, intermediate, AND advanced sections.
In my previous courses, I only focused on the advanced content (in-depth theory and implementations) which caused some students to feel left out.
In this course, we will cover ALL areas which are of interest to ANY student of Machine Learning or Data Science. It covers:
- Intuitions and Concepts for how Naive Bayes works (no math for beginners)
- Applications (the largest section, focused on a wide variety of areas like computer vision, natural language processing, genomics, finance, and healthcare)
- In-Depth (math and implementations for advanced students)
This is one of those algorithms that every ML engineer and data scientist simply must know. It’s powerful (under the right circumstances), it’s fast (update your model and get answers quickly), and it’s simple and interpretable (to explain your model to stakeholders). And yes, implementing Naive Bayes from scratch during a Data Scientist job interview is fair game!
With many tech companies laying off a significant portion of their workforce, it is now time to brush up on these skills.
Note: this will not be exclusive to deeplearningcourses.com, but other platforms will only get the “non-VIP” version which won’t contain 2/3 of the advanced implementations and the final section containing more applications, including CategoricalNB and ComplementNB.
So what are you waiting for?