Decision Trees for Data Science
Decision Trees Fundamentals and exploring ID3 and CART algorithms with real world application

What you will 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

Description

Unlock the potential of Decision Trees and elevate your data science skills with this comprehensive course. Decision Trees are a fundamental and versatile tool in the realm of machine learning, allowing you to make informed predictions and decisions based on complex datasets.

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:


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  • 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.

English
language

Content

Decision Trees – Supervised Machine Learning Algorithm

Agenda
What is DT, its intuition and Terminologies
Impurity Measures – Entropy, Gini Index and Classification Error
Decision Tree Algorithms and Lets learn ID3 DT
CART Decision Tree Algorithm – wrt Classification
CART Decision Tree Algorithm – wrt Regression
Implementation of CART using SKLearn Library
Use case on Decision Tree – Prediction of Wine Quality
Quiz on Fundamentals of Decision Tree Supervised Machine Learning Algorithm