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Distributed Machine Learning and Its Implementation with H2O
Gain insights into H2O-3's scalable framework and explore model interpretability, AutoML features, and algorithm implementation. Discover how to derive insights and tackle big data for explainable ML solutions.
37 Lessons
9h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of distributed machine learning
- Working knowledge of the powerful H2O ML framework, including various algorithms and their implementation in data science use cases
- Hands-on experience in building explainable machine learning models and deriving insights from them
- The ability to utilize autoML tools effectively for building fast, scalable, and robust machine learning models
Learning Roadmap
1.
Introduction to Machine Learning
Introduction to Machine Learning
Get familiar with machine learning basics, frameworks, and model evaluation techniques.
2.
Supervised Learning: Regression Models with H2O
Supervised Learning: Regression Models with H2O
Walk through setting up H2O clusters, regression models, EDA, and improving model accuracy.
3.
Supervised Learning: Classification Models with H2O
Supervised Learning: Classification Models with H2O
10 Lessons
10 Lessons
Go hands-on with classification models, exploratory data analysis, H2O AutoML, and model tuning.
4.
Unsupervised Learning: Clustering with H2O
Unsupervised Learning: Clustering with H2O
6 Lessons
6 Lessons
Enhance your skills in clustering using H2O, focusing on EDA, model prediction, and analysis.
5.
Unsupervised Learning: Anomaly Detection with H2O
Unsupervised Learning: Anomaly Detection with H2O
6 Lessons
6 Lessons
Solve problems in anomaly detection with H2O Isolation Forest algorithm.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
This course teaches about a distributed and highly scalable machine learning framework known as H2O-3. H2O has become the go-to solution for organizations seeking to build data-driven and explainable solutions. In this course, you’ll learn about H2O’s versatile machine learning framework, which empowers you with high-performing and interpretable machine learning models.
The H2O framework is compatible with Java, JSON, R, Python, and Scala. You’ll start by covering the concepts of machine learning models. As you progress, you’ll explore H2O’s core strengths: its focus on model interpretability and the efficiency-enhancing AutoML features. You’ll get to implement supervised and unsupervised algorithms and derive insights from them.
This course will equip you with expertise that will advance your career by making them valuable for tackling big data, creating explainable models, and automating ML tasks, opening doors in various industries.
ABOUT THE AUTHOR
Tarique Husaain
Senior Data Scientist @ H2O.ai | IIT Kharagpur | Kaggle Competitions Master
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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