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Supervised Machine Learning with Tree Based Models

star 4.56  Beginner level 3.0 learning hrs 9.9K+ Learners

Instructor:

Mr. Bharani Akella

Key Highlights

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About this course

We understand Machine Learning, a subset of Artificial Intelligence, as a computer being programmed with the ability to self-learn and improve itself on a particular task. Supervised Learning in Machine Learning allows one to produce or collect data based on previous experience. It helps one to optimize performance criteria using past experience and work on real-time computational problems. As making Decisions and finding insights from raw data is an essential part of Machine Learning, Tree-based classifiers are widely used for this purpose. Understanding the importance of tree-based classifiers, we have curated this course on tree-based classifiers, which will help you understand various types of supervised learning techniques, such as decision trees, random forests, and how to implement them in Python. The skills covered in this course are NumPy, Pandas, and Scikit Learn Library. 

Great Learning has collaborated with the best universities, such as the University of Texas at Austin and SRM Institute of Science and Technology, and created the top AI Courses in India. World-class professors have prepared a comprehensive and exhaustive curriculum to provide learners with a world-class education to develop advanced skills in the online AIML course. The learners will also receive personalized mentorship from industry experts who are working at top organizations.

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Course outline

Introduction to Decision Tree

Demo for Decision Tree

Introduction to Random Forest

Demo for Random Forest

Get access to the complete curriculum once you enroll in the course

Supervised Machine Learning with Tree Based Models

rating icon 4.56

3.0 Hours

Beginner

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9.9K+ learners enrolled so far

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Get free course content

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Master in-demand skills & tools

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Test your skills with quizzes

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Learner reviews of the Free Courses

4.56
71%
22%
5%
1%
1%
Reviewer Profile

4.0

Country Flag India
“An Engaging and Informative Course Experience”
I thoroughly enjoyed this course and found it to be highly informative and engaging. The curriculum was well-structured, covering a wide range of topics in depth. The instructors were knowledgeable and presented the material in a clear and concise manner. The quizzes and assignments were challenging yet fair, helping to reinforce the concepts learned. Overall, this course provided a comprehensive learning experience that was both enjoyable and educational.
Reviewer Profile

5.0

Country Flag Nigeria
“Highlight of Your Learning Experience”
I loved how the course combined videos, quizzes, and hands-on activities to keep things interesting and cater to different learning styles. It was a comprehensive experience that not only increased my knowledge but also built my confidence in applying these skills.
Reviewer Profile

5.0

Country Flag India
“skills and tools and also instructor guiding”
The curriculum includes a comprehensive study of data analysis techniques, including the use of decision trees and random forests for classification and regression tasks. Students will learn how to implement these algorithms using Python libraries like scikit-learn. Additionally, the course covers evaluating models with confusion matrices and understanding their applications in real-world data scenarios.
Reviewer Profile
Abdullah Sheikh

4.0

“The learning experience of supervised machine learning involves collecting and preparing labeled data, selecting and training a model to learn from this data”
The learning experience of supervised machine learning involves collecting and preparing labeled data, selecting and training a model to learn from this data, and evaluating its performance using appropriate metrics. This process is iterative, requiring adjustments based on testing results and ongoing monitoring to ensure model accuracy. Ultimately, it aims to effectively predict outcomes for unseen data.
Reviewer Profile

4.0

Country Flag India
“Supervised machine learning with tree-based models is a powerful approach for classification and regression tasks. ”
Key Concepts Supervised Learning: In supervised learning, the model is trained on a labeled dataset, meaning that the input data comes with corresponding output labels. The goal is to learn a mapping from inputs to outputs. Tree-Based Models: These models use a tree-like structure to make decisions based on the features of the input data. The tree is constructed by splitting the data into subsets based on feature values.
Reviewer Profile

5.0

“Learned this module good for my future experiences”
A good course is one that provides clear, structured content that is engaging and relevant to the learners' goals. It should offer practical knowledge or skills, include interactive elements like exercises or discussions, and provide opportunities for feedback and assessment. The course should be well-paced, accessible, and support various learning styles to ensure a positive and effective learning experience.
Reviewer Profile

5.0

Country Flag India
“Supervised Machine Learning with Tree-Based Models”
This helped in learning the usage of Python in machine learning.
Reviewer Profile

5.0

Country Flag India
“Supervised Machine Learning with Tree Based Models”
Good course to quickly learn Decision trees and Random Forest basics
Reviewer Profile

4.0

“I gained immense knowledge about machine learning and its related subjects. This is the best free course I've found”
I gained immense knowledge about machine learning and its related subjects. This is the best free course I've found
Reviewer Profile

5.0

Country Flag Australia
“it was really a good experience to learn some new course at great learning platform”
it was really a good experience to learn some new course at great learning platform

Our course instructor

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Mr. Bharani Akella

Data Scientist

Machine Learning Expert

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5M+ Learners
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125 Courses
Bharani has been working in the field of data science for the last 2 years. He has expertise in languages such as Python, R and Java. He also has expertise in the field of deep learning and has worked with deep learning frameworks such as Keras and TensorFlow. He has been in the technical content side from last 2 years and has taught numerous classes with respect to data science.

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