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Distributed Machine Learning and Its Implementation with H2O

Name: Distributed Machine Learning and Its Implementation with H2O
Price: 59 USD
Author: Tarique Husaain

Gain insights into H2O-3's scalable framework, model interpretability, AutoML features, and algorithm implementation. Learn to derive insights and tackle big data for explainable ML solutions.

37 lessons

Created by Tarique Husaain

Distributed Machine Learning and Its Implementation with H2O

What's included

37 lessons across 7 modules
6 quizzes with instant feedback
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

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

37 Lessons6 Quizzes

Introduction to Machine Learning

Get familiar with machine learning basics, frameworks, and model evaluation techniques.

Let’s Dive In

Machine Learning Concepts

Evaluating Machine Learning Models

Machine Learning Frameworks

Quiz: Machine Learning Concepts

Supervised Learning: Regression Models with H2O

Walk through setting up H2O clusters, regression models, EDA, and improving model accuracy.

Regression with H2O

Exploratory Data Analysis

Setting Up the H2O Cluster

Regression Model and Prediction

Regression Model Explanation

Challenge: Improve H2OXGBoost Regression Model

Solution: Improve H2OXGBoost Regression Model

Quiz: H2O Regression Concepts

Supervised Learning: Classification Models with H2O

10 Lessons

Go hands-on with classification models, exploratory data analysis, H2O AutoML, and model tuning.

Unsupervised Learning: Clustering with H2O

6 Lessons

Enhance your skills in clustering using H2O, focusing on EDA, model prediction, and analysis.

Unsupervised Learning: Anomaly Detection with H2O

6 Lessons

Solve problems in anomaly detection with H2O Isolation Forest algorithm.

Closing Notes

Investigate key concepts and practical skills in machine learning using the H2O framework.

Wrap Up

Appendix

Prepare for H2O by ensuring compatible OS, Java, and optional R or Python integrations.

H2O Installation

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE 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

Learn more about Tarique

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