Each project comes with 2-5 hours of micro-videos explaining the solution.

Get access to 50+ solved projects with iPython notebooks and datasets.

Add project experience to your Linkedin/Github profiles.

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

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*.

In this data science project, you will learn to predict churn on a built-in dataset using Ensemble Methods in R.

Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

Deep Learning Project- Learn about implementation of a machine learning algorithm using autoencoders for anomaly detection.

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