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

  • What is H2O and why is it used

  • Initializing an H2O cluster for using all cores of CPU and RAM

  • Importing the dataset from amazon AWS

  • Performing basic EDA on the dataset

  • Visualizing the dataset using Quantiles and Histograms

  • Constructing test and train sets using sampling

  • Defining the data for the model and displaying the results

  • Applying GLM model for making predictions

  • Defining mean_squared_error for evaluation metrics

  • View model information: training statistics, performance, important variables

  • Defining the parameters for a deep leaning Neural Networks

  • The first part of the data, without labels for unsupervised learning

  • The second part of the data, with labels for supervised learning

  • Converting train dataset with autoencoder model to lower-dimensional space

  • Training the DRF on reduced feature space

  • Making the final predictions

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.

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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