Getting Started with Decision Trees

Learn the basics of Decision Trees - a popular and powerful machine learning algorithm and implement them using Python
4.21 (34 reviews)
Udemy
platform
English
language
Data Science
category
Getting Started with Decision Trees
2β€―685
students
1 hour
content
Feb 2020
last update
$19.99
regular price

Why take this course?

πŸŽ“ Course Title: Getting Started with Decision Trees

Headline: 🌳 Master the Basics of Decision Trees - A Powerful Tool in Machine Learning with Python!


Course Description:

Dive into the fascinating world of machine learning with our comprehensive course on Decision Trees. This popular and powerful algorithm is a staple in the data scientist's toolkit, offering a robust solution to complex problems. Whether you're an aspiring analyst or a seasoned engineer looking to brush up your skills, this course will equip you with the knowledge to harness the full potential of Decision Trees.

Why learn about Decision Trees? πŸŽ“

  • Widely Used: Discover why Decision Trees are the most popular machine learning algorithm across industries.
  • Versatility: Learn how these trees can tackle both classification and regression tasks with equal finesse.
  • Ease of Interpretation: Understand the significance of decision trees for stakeholders by presenting solutions in a clear, interpretable format.

Course Highlights:

  • Introduction to Decision Trees: Get acquainted with the fundamental concepts and applications of decision trees.
  • Terminologies Related to Decision Trees: Familiarize yourself with key terms that form the vocabulary of decision tree analysis.
  • Splitting Criterion: Explore different splitting criteria, such as Gini impurity and chi-square distribution, which are pivotal in building an effective decision tree.
  • Implementation in Python: Gain hands-on experience by implementing a decision tree from scratch using Python's powerful libraries like scikit-learn.

By the end of this course, you will have a solid understanding of Decision Trees and be able to confidently apply this knowledge to real-world data science challenges. Whether you're predicting customer churn, determining credit risk, or classifying species in a botanical dataset, decision trees offer a clear and effective approach to complex problems.

Embark on your journey to becoming a data science expert today with Getting Started with Decision Trees. πŸš€


What's Covered in the Course?

  1. Introduction to Decision Trees:

    • Understand the concept and the role of decision trees in machine learning.
    • Learn about the historical context and development of decision trees.
  2. Terminologies Related to Decision Trees:

    • Get to grips with essential terms like nodes, splits, leaves, branches, and the tree structure.
    • Dive into the mechanics of how a decision tree learns from data.
  3. Different Splitting Criteria for Decision Trees:

    • Compare and contrast various splitting criteria: Gini impurity, entropy, chi-square, and others.
    • Understand the pros and cons of each criterion and when to use them effectively.
  4. Implementation in Python:

    • Follow step-by-step tutorials to build a decision tree model using Python.
    • Utilize Python's scikit-learn library for real-world implementation and problem-solving.
    • Practice with datasets provided within the course, enhancing your understanding through application.

Join us now and unlock the door to effective data analysis and decision-making with Decision Trees! 🌳✨

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Related Topics

2787320
udemy ID
30/01/2020
course created date
14/02/2020
course indexed date
Lee Jia Cheng
course submited by