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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
00:03:05
  Introduction to H2O
00:06:52
  Create Histogram
00:00:23
  H2O Operations
00:02:32
  Predictions
00:02:36
  Data Preparation
00:02:06
  Machine Learning Models using H2O
00:00:32
  Deep Learning using H2O - Basic Model
00:07:35
  Deep Learning using H2O - Adding Epochs
00:02:58
  Deep Learning using H2O - Stopping Criteria
00:04:43
  Deep Learning using H2O - Hidden Layer
00:00:28
  Compare Performance of Models
00:02:00
  Cross Validation
00:03:01
  Grid Search
00:26:44
  Supervised VS Unsupervised
00:07:12
  AutoEncoder Model for feature reduction
00:03:13
  Conclusion
00:03:42