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.

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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.

Distributed Machine Learning and Its Implementation with H2O

A classification model correctly predicts that a certain disease is present in 70 patients out of 500, of which 120 actually have the disease. What is the recall of the model?

A classification model correctly predicts that a certain disease is present in 70 patients out of 500, of which 120 actually have the disease. What is the recall of the model?

The model predicts a negative class and the true label is also negative.

The model predicts a negative class and the true label is positive.

The model predicts a positive class and the true label is also positive.

The model predicts a positive class and the true label is negative.

In a binary classification problem, a false positive occurs when:

In a binary classification problem, a false positive occurs when:

Which error metric provides an estimate of the proportion of the variance in the dependent variable that is explained by the independent variable?

Which error metric provides an estimate of the proportion of the variance in the dependent variable that is explained by the independent variable?

Rank the following features in decreasing order of importance for building a classification model to predict loan defaulters:

a. Gender of the loan applicant

b. Occupation of the loan applicant

c. Credit score of the loan applicant

d. Income level of the loan applicant


Rank the following features in decreasing order of importance for building a classification model to predict loan defaulters:
a. Gender of the loan applicant
b. Occupation of the loan applicant
c. Credit score of the loan applicant
d. Income level of the loan applicant

Arrange the following features in decreasing order of importance for building a regression model to predict a student's performance on an exam:

a. Student’s attendance

b. Student’s past academic performance

c. Number of hours spent studying per day

d. Distance of the student’s home from school

Arrange the following features in decreasing order of importance for building a regression model to predict a student’s performance on an exam:
a. Student’s attendance
b. Student’s past academic performance
c. Number of hours spent studying per day
d. Distance of the student’s home from school

Test your knowledge, understanding, and retention of information.

Introduction to Machine Learning

Supervised Learning: Regression Models with H2O

Supervised Learning: Classification Models with H2O

Unsupervised Learning: Clustering with H2O

Unsupervised Learning: Anomaly Detection with H2O

Closing Notes

Appendix

Quiz: Machine Learning Concepts