Home » Development » Decision Trees for Data Science

[Free] Decision Trees For Data Science

Decision Trees Fundamentals and exploring ID3 and CART algorithms with real world application – Free Course

Added on May 1, 2025 Development 2 min read

What you’ll learn

• Understand the Foundations of Decision Trees
• Master Decision Tree Algorithms and Techniques
• Apply Decision Trees to Real-World Scenarios
• Comprehend Ensemble Learning with Decision Trees

Requirements

• Familiarity with fundamental machine learning concepts, such as supervised learning, classification, and regression, will provide a solid foundation for understanding decision trees.
• Basic programming skills in a language commonly used for machine learning, such as Python or R, will be beneficial. Ensure that learners are comfortable with writing and running code
• A basic understanding of statistical concepts, such as probability and descriptive statistics, will help learners grasp the principles behind decision tree algorithms and their application.
• Knowledge of how to handle and preprocess data is important. Familiarity with tasks like data cleaning, feature engineering, and data visualization will enhance the learning experience
• Familiarity with popular machine learning libraries or frameworks, such as scikit-learn for Python or caret for R, would be advantageous. Ensure that learners can navigate and use these tools.

Description

In this course, you will embark on a journey from the basics to advanced applications of Decision Trees in data science. Starting with the foundational principles, you’ll understand the inner workings of decision nodes, branches, and leaves. You will delve into the intricacies of various decision tree algorithms, including ID3, C4.5, and CART, learning how to choose the right algorithm for different scenarios.

Key Topics Covered:

• Understanding decision tree fundamentals

• Exploring decision tree algorithms: ID3, C4.5, CART

• Hands-on construction and optimization of decision trees

• Real-world applications in classification and regression

• Handling missing values and data preprocessing

• Ensemble learning with Random Forests and Gradient Boosting

• Practical insights for avoiding overfitting

• Interpretability and visualization of decision trees

• Applications of decision trees in diverse industries

By the end of this course, you’ll not only have a solid grasp of Decision Trees but also the confidence to apply this powerful tool to a variety of data science challenges. Whether you’re a beginner or an experienced data professional, this course is your gateway to mastering Decision Trees for impactful data-driven decision-making.

Enroll now and elevate your data science journey with the precision and intelligence of Decision Trees.