Solving Multiple Classification use cases Using H2O

In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.

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What will you learn

Introduction to H2O

Data cleaning using H2O

Model Training using H2O

Model scalability using H2O in Hadoop environment

Driverless AI using H2O

What will you get

Access to recording of the complete project

Access to all material related to project like data files, solution files etc.

Prerequisites

Knowledge of Data Science in R and Machine Learning algorithms.

The language used: R-studio, R and H2O.

Project Description

H2O.ai is focused on bringing AI to businesses through software.

H2O includes many common Machine Learning algorithms, such as generalized linear modeling (linear regression, logistic regression, etc.), Naive Bayes, principal components analysis, k-means clustering, and word2vec. H2O implements best-in-class algorithms at scale, such as distributed random forest, gradient boosting, and deep learning. H2O also includes a Stacked Ensembles method, which finds the optimal combination of a collection of prediction algorithms using a process known as stacking.

Instructors

Pradeepta

Curriculum For This Mini Project

Install and Initialize H2O Library

03m

Introduction to H2O

06m

Create Histogram

00m

H2O Operations

02m

Predictions

02m

Data Preparation

02m

Machine Learning Models using H2O

00m

Deep Learning using H2O - Basic Model

07m

Deep Learning using H2O - Adding Epochs

02m

Deep Learning using H2O - Stopping Criteria

04m

Deep Learning using H2O - Hidden Layer

00m

Compare Performance of Models

02m

Cross Validation

03m

Grid Search

26m

Supervised VS Unsupervised

07m

AutoEncoder Model for feature reduction

03m

Conclusion

03m