Fundamentals of gradient Boosting
Unlock the Power of Advanced Machine Learning: Master Decision Trees, Random Forests, and Gradient Boosting!
Gain a thorough understanding of the math and intuition behind Gradient Boosting.
Learn to avoid overfitting by implementing improvements to basic Gradient Boosting algorithms.
Get hands-on experience with real-world examples, code, and best practices.
Still pondering? If you're keen to bring your predictive modeling game to the next level, this course is the stepping stone you've been searching for.
Principal | Numeristical
Brian Lucena,PhD
Brian Lucena is Principal at Numeristical and the creator of StructureBoost, ML-Insights, and SplineCalib. His mission is to enhance the understanding and application of modern machine learning and statistical techniques. He does this through academic research, open-source software development, and educational content such as live stream classes and interactive Jupyter notebooks. Additionally, he consults for organizations of all sizes from small startups to large public enterprises. In previous roles, he has served as SVP of Analytics at PCCI, Principal Data Scientist at Clover Health, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
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1
Welcome to the course!
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Welcome to the course!
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What you'll learn in this course
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Course Notebook
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How to use this course
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2
Section 1: Decision Trees
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Decision Trees Video Lesson
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3
Section 2: Random Forests
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Random Forests Video Lesson
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4
Section 3: Gradient Boosted Trees
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Gradient Boosted Trees - Video Lesson
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5
Section 4: Details of Hyperparameters
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Details of Hyperparameters - Video Lesson
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Learning Objectives
By the end of this course, you should be able to:
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Understand how to analyze network traffic, including what features to extract and how to analyze them.
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Use Python and Scapy to analyze network traffic in packet capture files and live captures.
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Develop custom Python scripts to answer questions with network traffic data.
This course starts with a single decision tree and progresses to random forests and gradient-boosted tree models. We cover the various hyperparameters involved in gradient-boosted tree models and characterize their importance. We also discuss why gradient boosting can generally outperform linear/logistic regression.
- Module 1: Decision Trees
- Module 2: Random Forests
- Module 3: Gradient Boosted Trees
- Module 4: Details of Hyperparameters
Background knowledge needed
Helpful, but not required to know.
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Knowledge of Python
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Network Traffic Analysis
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Data Science
Is the "Fundamentals of Gradient Boosting" course right for you? If you're keen on delving into machine learning algorithms and understanding how to sequentially ensemble weak predictive models into stronger ones, this course is your go-to resource.