Overview
Master a crucial artificial intelligence algorithm and skyrocket your Python programming skills
Syllabus
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- 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
Tags
On-Demand
