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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