Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Decision Trees for Data Science
Rating: 4.3 out of 5(14 ratings)
1,322 students

Decision Trees for Data Science

Decision Trees Fundamentals and exploring ID3 and CART algorithms with real world application
Created byPralhad Teggi
Last updated 12/2023
English

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

Course content

1 section • 8 lectures • 1h 14m total length

  • Agenda1:49
  • What is DT, its intuition and Terminologies12:57
  • Impurity Measures - Entropy, Gini Index and Classification Error19:25
  • Decision Tree Algorithms and Lets learn ID3 DT17:11

    Discover how decision trees divide input space using impurity measures such as entropy and information gain, and learn ID3 for classification, including splitting, pruning, and tree construction.

  • CART Decision Tree Algorithm - wrt Classification7:00

    Explore how the CART decision tree performs binary splits for classification and regression, using Gini impurity and variance, with pruning to balance underfit and overfit.

  • CART Decision Tree Algorithm - wrt Regression6:11
  • Implementation of CART using SKLearn Library2:40
  • Use case on Decision Tree - Prediction of Wine Quality7:32
  • Quiz on Fundamentals of Decision Tree Supervised Machine Learning Algorithm

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

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:

  • 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.

Who this course is for:

  • Individuals who are just starting their journey in data science and machine learning and want to understand the basics of decision trees as a predictive modeling technique.
  • Professionals working with data analysis who want to expand their skills to include machine learning techniques like decision trees for classification and regression tasks.
  • Programmers and software developers interested in incorporating machine learning into their applications or gaining a better understanding of how decision trees work.
  • Students studying data science, computer science, or related fields who want to deepen their knowledge of machine learning algorithms, specifically decision trees.
  • Enthusiasts and lifelong learners who have a general interest in machine learning and want to explore decision trees as a part of their broader understanding of the field.

Report abuse