Data Science & Machine Learning: Naive Bayes in Python

Overview

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:

Syllabus

• Introduction to Naive Bayes
Overview of Naive Bayes Algorithm
Applications of Naive Bayes in Real-World Scenarios
• Probability Foundations
Basics of Probability Theory
Understanding Conditional Probabilities
• The Naive Bayes Classifier
Assumptions Behind Naive Bayes
Types of Naive Bayes Classifiers
Advantages and Disadvantages
• Implementing Naive Bayes in Python
Setting Up Your Python Environment
Importing Necessary Libraries (e.g., NumPy, pandas, scikit-learn)
Writing a Simple Naive Bayes Classifier from Scratch
• Text Classification with Naive Bayes
Preprocessing Text Data
Implementing Multinomial Naive Bayes for Text Classification
Case Study: Spam Detection
• Naive Bayes for Continuous Features
Gaussian Naive Bayes
Application to Real-World Data (e.g., Iris Dataset)
• Evaluating Model Performance
Confusion Matrix
Precision, Recall, and F1-Score
Cross-Validation Techniques
• Advanced Topics and Variants
Bernoulli Naive Bayes
Complement Naive Bayes
Handling Missing Data
• Practical Projects and Case Studies
Case Study: Sentiment Analysis on Social Media Posts
Project: Predicting Customer Behavior in E-commerce
• Course Summary and Next Steps
Recap of Key Concepts
Further Reading and Resources
Advanced Topics in Machine Learning and Data Science

---

Taught by

Lazy Programmer Inc. and Lazy Programmer Team

---

Subjects

Data Science