Decision Trees for Data Science

Decision Trees for Data Science, available at Free, has an average rating of 4.5, with 8 lectures, 1 quizzes, based on 6 reviews, and has 718 subscribers.

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You will learn about 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 This course is ideal for individuals who are 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. or Professionals working with data analysis who want to expand their skills to include machine learning techniques like decision trees for classification and regression tasks. or Programmers and software developers interested in incorporating machine learning into their applications or gaining a better understanding of how decision trees work. or Students studying data science, computer science, or related fields who want to deepen their knowledge of machine learning algorithms, specifically decision trees. or 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. It is particularly useful 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. or Professionals working with data analysis who want to expand their skills to include machine learning techniques like decision trees for classification and regression tasks. or Programmers and software developers interested in incorporating machine learning into their applications or gaining a better understanding of how decision trees work. or Students studying data science, computer science, or related fields who want to deepen their knowledge of machine learning algorithms, specifically decision trees. or 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.

Enroll now: Decision Trees for Data Science

Summary

Title: Decision Trees for Data Science

Price: Free

Average Rating: 4.5

Number of Lectures: 8

Number of Quizzes: 1

Number of Published Lectures: 8

Number of Published Quizzes: 1

Number of Curriculum Items: 9

Number of Published Curriculum Objects: 9

Original Price: Free

Quality Status: approved

Status: Live

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

Who Should Attend

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.

Target Audiences

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.

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.

Course Curriculum

Chapter 1: Decision Trees – Supervised Machine Learning Algorithm

Lecture 1: Agenda

Lecture 2: What is DT, its intuition and Terminologies

Lecture 3: Impurity Measures – Entropy, Gini Index and Classification Error

Lecture 4: Decision Tree Algorithms and Lets learn ID3 DT

Lecture 5: CART Decision Tree Algorithm – wrt Classification

Lecture 6: CART Decision Tree Algorithm – wrt Regression

Lecture 7: Implementation of CART using SKLearn Library

Lecture 8: Use case on Decision Tree – Prediction of Wine Quality

Instructors

Pralhad Teggi
Solution Architect and Research Scholar in AI and ML Area

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4 stars: 3 votes

5 stars: 3 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!