Deep Learning with H2O & R Training

Live Online & Classroom Enterprise Training

Master core concepts of H2O - an accurate, fast and scalable deep learning platform. Build as many models as you like and compare the results to find the best. Be ready to deploy smart apps with minimal coding effort by leveraging the built-in ML models.

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Key Features
  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

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What is H2O Deep Learning Course about?

Gain firsthand expertise on installing, configuring, and deploying H2O and make use of the R APIs to handle billions of rows of data without sampling and get accurate predictions faster. Through practical guided exercises leverage the built-in machine learning algos such as generalized linear modeling (linear regression, logistic regression, etc.), Naïve Bayes, principal components analysis, time series, k-means clustering, Random Forest, Gradient Boosting, and Deep Learning at scale.

In cloud labs, practice implementing GBM, Random Forest, GLM, GLRM and become familiar with concepts such as Stacking and Super Learning.

Be the expert of deploying complex deep learning models using H2O with R.


Suggested Audience

  • Developers adding machine learning to apps
  • Data Scientists/DevOps putting models into production

What are the objectives of H2O Deep Learning Course ?

This Deep Learning with H2O with R training course enables you to:

  • Install and configure H2O to work with R, Python, Cloud Providers
  • Gain a deep understanding of built-in Machine Learning models and usage Access
  • H2O features through APIs Build and Train multiple models on a single node or in a cluster
  • Train a generalized linear model, generalized low rank models Install and use H2O
  • Ensemble to load, train, evaluate model performance Using Storm with H2O for real time prediction
  • Deliver scalable models that can work on the complex and large datasets



Required: Working knowledge of Java, R, Storm, Machine Learning, Deep Learning Models

Available Training Modes

Live Online Training

12 Hours

Classroom Training


2 Days

Who is H2O Deep Learning Course for?

  • Anyone who wants to add H2O Deep Learning skills to their profile
  • Teams getting started on H2O Deep Learning projects
  • What are the prerequisites for H2O Deep Learning Course?

    Course Outline

    • Introduction
      • Data Science
      • H2O
      • Building a Smarter Application
      • Combining applications with models
      • Deploying models into production
    • Installing H2O
      • Downloading and Unzipping H2O Package
      • Installing H2O from within R
      • Installing H2O from within Python
      • H2O Quickstart with R
      • H2O Cloud Integration
    • Deep Learning
      • H2O R Package
      • Start H2O
      • Decision Boundaries
      • Cover Type Dataset
      • - Exploratory Data Analysis
      • - Deep Learning Model
      • - Hyper-Parameter Search
      • - Checkpointing
      • - Cross Validation
      • - Model Save & Load
    • Regression and Binary Classification
      • Regression and Binary Classification
    • Unsupervised Anomaly Detection
      • Unsupervised Anomaly Detection
    • GBM & Random Forest
      • Decision Trees
      • Random Forest
      • Gradient Boosted Machines
      • H2O Implementation
    • Generalized Linear Model (GLM)
      • Cover Type
      • Multinomial Model
      • Binomial Model
    • Generalized Low Rank Models (GLRM)
      • Introduction
      • Basic Model Building(Example)
      • Plotting Archetypal Features
      • Imputing Missing Values
    • Hive UDF Plain Old Java Object (POJO) Example
      • Load Training & Test Data
      • Create Models
      • Export the best model as POJO
      • Compile the H2O model as part of the UDF Project
      • Copy the UDF to the cluster and load into Hive
      • Score with your UDF
    • Ensembles: Stacking, Super Learner
      • Bagging
      • Boosting
      • Stacking / Super Learning
      • Install H2O Ensemble
      • Higgs Demo
      • Start H2O Cluster
      • Load Data into H2O Cluster
      • Specify Base Learner & Metalearner
      • Train an Ensemble
      • Evaluate Model Performance
      • Predict
    • Streaming: Real-time Predictions with H2O on Storm
      • Installing the required software
      • A brief discussion of the data
      • Using R to build a gbm model in H2O
      • Exporting the gbm model as a Java POJO
      • Copying the generated POJO files into a Storm bolt build environment
      • Building Storm and the bolt for the model
      • Running a Storm topology with your model deployed
      • Watching predictions in real-time

    Who is the instructor for this training?

    The trainer for this Deep Learning with H2O & R Training has extensive experience in this domain, including years of experience training & mentoring professionals.