Intermediate

Decision Tree Algorithms for Machine Learning

Name: Decision Tree Algorithms for Machine Learning
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Decision Trees are a machine learning method that uses a tree-like structure of conditions to make predictions. By splitting data based on features, they create clear decision paths, making them easy to understand and useful for classification and prediction tasks.

4.4

|3h 9m

Decision Tree Algorithms for Machine Learning course

What's Included

Lessons

3h 9m

Duration

Certificate

What You'll Master

Skills and outcomes you'll walk away with

Module 1: Foundational Theory and the Anatomy of Decision Trees

Module 2: Mathematical Foundations - Splitting Criteria

Module 3: Classic Tree Algorithms - ID3, C4.5, and CART

Module 4: Practical Implementation with Python and Scikit-Learn

Module 5: Overfitting and Tree Optimization Techniques

Module 6: Visualization and Model Interpretability

Course Curriculum

10 lessons • 3h 9m total

Module 1: Foundational Theory and the Anatomy of Decision Trees

19m

Module 2: Mathematical Foundations - Splitting Criteria

15m

Module 3: Classic Tree Algorithms - ID3, C4.5, and CART

20m

Module 4: Practical Implementation with Python and Scikit-Learn

17m

Module 5: Overfitting and Tree Optimization Techniques

17m

Module 6: Visualization and Model Interpretability

19m

Module 7: Ensemble Methods I - Bagging and Random Forests

22m

Module 8: Ensemble Methods II - Boosting Architectures

18m

Module 9: Advanced Specialized Topics and Oblique Trees

22m

Module 10: Real-World Applications and Industry Case Studies

20m

Certification Path

Certification Exam

30 multiple-choice questions • 70% passing score required

Final Project: Building an SMS Spam Classifier using Naive Bayes

In this final assignment, you will apply the concepts learned throughout the course to build a robust SMS spam detection system. You are required to: 1) Load and preprocess the SMS Spam Collection dataset through cleaning text, removing stop words, and tokenization. 2) Implement a Multinomial Naive Bayes classifier. 3) Split the data into training and testing sets. 4) Evaluate the model using Accuracy, Precision, Recall, and an F1-score. 5) Provide a brief report discussing why Naive Bayes is suitable for this specific task despite the naive assumption of feature independence.

Verified Certificate

Earn a verified PDF certificate with unique verification ID upon completion • ₹199