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Free Decision Tree Course for Beginners

Introduction to Decision Trees

star 4.45  Beginner level 1.5 learning hrs 990 Learners

Learn decision tree from basics in this free online training. Decision tree course is taught hands-on by experts. Learn about introduction to decision tree along with examples of decision tree & lot more.

Introduction to Decision Trees

star 4.45  Beginner level 1.5 learning hrs 990 Learners
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Skills you will gain

Entropy

Loss Function

Information Gain

Key Highlights

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About this course

Tree-based models are really important in the field of machine learning because we can perform both regression and classification with them. In this course, we shall comprehensively learn about tree based models. We shall start off by looking at the decision tree structure. Then we shall learn about concepts such as Gini Index, Entropy, Loss Function and Information Gain. Finally, we shall also look at some advantages and disadvantages of decision trees. Overall, this course will get you started with all the fundamentals about the tree based models.

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Course outline

Introduction to Decision Trees Part 1

Introduction to Decision Trees Part 2

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Introduction to Decision Trees

rating icon 4.45

1.5 Hours

Beginner

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990 learners enrolled so far

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Learner reviews of the Free Courses

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Reviewer Profile
Joshua Senteu

5.0

★★★★ ★
“Comprehensive Introduction to Decision Trees”
I thoroughly enjoyed the Introduction to Decision Tree course. The explanations were clear and concise, covering the fundamentals of decision trees, including their structure, splitting criteria, and applications in classification and regression. The course provided excellent visualizations to understand how splits are made based on features and how the tree evolves. The hands-on examples made the learning experience engaging and practical.
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Reviewer Profile
Naduni Dewrangi Athukorala

5.0

★★★★ ★
“Very valuable course which is offered by great learning”
The course provided a solid introduction to decision tree algorithms, covering essential concepts like entropy, Gini Index, and information gain in a clear and easy-to-understand way. The practical examples and real-life applications of decision trees for both classification and regression tasks helped solidify the theoretical knowledge.
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Reviewer Profile
Hirishanth Jegatheeswaran

5.0

★★★★ ★
“Great Learning ExperienceEngaging Content: The material was well-structured and engaging, making complex concepts easy to understand.”
Engaging Content: The material was well-structured and engaging, making complex concepts easy to understand. Interactive Sessions: The interactive elements, such as quizzes and discussions, really enhanced my learning experience. Supportive Instructors: Instructors were knowledgeable and approachable, providing valuable insights and assistance when needed. Practical Applications: I appreciated the real-world examples that helped to illustrate the concepts we were learning.
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Reviewer Profile
Albert Yeo Boon Leong

5.0

★★★★ ★
Singapore
“The lessons are instructive and easy to follow”
Even without prior knowledge and experience in this area, I can still follow the lessons.
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Reviewer Profile
Shawn Chia

5.0

★★★★ ★
Singapore
“Feedback on this particular course module”
Feedback on this particular course module is interesting.
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Reviewer Profile
Syeda Maria Azfar

5.0

★★★★ ★
“In Depth Explanation of Introduction to Decision Trees”
In Depth Explanation of Introduction to Decision Trees and Machine Learning
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Reviewer Profile
Nguyễn Hoàng Anh Khôi

5.0

★★★★ ★
“Categorical values and continues values Random numbers Binary values None of the aboveCategorical values and continues values Random numbers Binary values None of the above”
Categorical values and continues values Random numbers Binary values None of the aboveCategorical values and continues values Random numbers Binary values None of the aboveCategorical values and continues values Random numbers Binary values None of the aboveCategorical values and continues values Random numbers Binary values None of the aboveCategorical values and continues values Random numbers Binary values None of the above
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Reviewer Profile
NOOR UL AIN Arshad

5.0

★★★★ ★
“The model is a form of supervised learning, meaning that the model is trained and tested on a set of data that contains the desired categorization.”
A decision tree is a type of supervised machine learning used to categorize or make predictions based on how a previous set of questions were answered. The model is a form of supervised learning, meaning that the model is trained and tested on a set of data that contains the desired categorization.
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Reviewer Profile
Abdullah Durrani

5.0

★★★★ ★
“Basics of python, For example Python Seaborn”
Decision trees can indeed be used for regression tasks. In a regression tree, the model predicts continuous values rather than class labels. Regression trees split the data based on minimizing the variance in the target variable, making them effective for problems like predicting prices, quantities, or any other continuous outcome.
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Reviewer Profile
Aerfan Waziri

5.0

★★★★ ★
“Good stuff and a lot of important in formation”
i liked the fact that the topics were covered properly. very nice indeed!
Read More

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