Udemy Coupon for Data Science & Machine Learning: Naive Bayes in Python. Find Out Other Data Science Courses and Tutorials from Udemy Learning with Discount Coupon Codes. Master a crucial artificial intelligence algorithm and skyrocket your Python programming skills
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Data Science & Machine Learning: Naive Bayes in Python
In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as:
- computer vision
- natural language processing
- financial analysis
- healthcare
- genomics
Why should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.
This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You’ll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You’ll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.
In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!
Thank you for reading and I hope to see you soon!
In this Data Science Course you will learn?
- Apply Naive Bayes to image classification (Computer Vision)
- Apply Naive Bayes to text classification (NLP)
- Apply Naive Bayes to Disease Prediction, Genomics, and Financial Analysis
- Understand Naive Bayes concepts and algorithm
- Implement multiple Naive Bayes models from scratch
Data Science & Machine Learning
- Instructor: Lazy Programmer
- Duration: 7 hours 50 minutes
- Language: English
- Source: Udemy
