These slides were presented by Mateusz Dymcyzk at the our Sydney AI and deep learning meetup. The authors were Sudalai Rajkumar (SRK), Data Scientist at H2O.ai and Nikhil Shekhar, ML Engineer at H2O.ai.

1. H2O Driverless AIMachine Learning with H2O
Sudalai Rajkumar @ SRK
Data Scientist
Nikhil Shekhar
ML Engineer

2. What is Machine Learning?

3. Sunny or Rainy?

4. Sunny or Rainy?
Machine
Learning
Algorithm

5. Types of Machine Learning

6. Applications of Machine Learning
Fraud Detection
Text classificationTranslation
Image classification Recommendation

7. About H2O.ai

8. H2O

9. H2O - Key Features
• Leading Algorithms
• Access from Python, R and Flow
• AutoML
• Distributed, In-memory processing
• Simple deployment

10. Sparkling Water

11. Sparkling Water - Key Features
• Access to H2O algorithms
• Drive computation from Scala, R, Python, Flow
• Simple deployment

12. H2O4GPU

13. H2O4GPU - Key Features
• Optimized for GPU performance
• Broad selection of GPU enabled algorithms
• Builds on scikit-learn python API
• Available R API

14. Driverless AI

15. Driverless AI - Key Features
• Automatic feature engineering
• Automatic model tuning
• Machine Learning Interpretability
• Automatic visualization
• Automatic scoring pipeline
• GPU acceleration
• Time series support

16. Installing H2O

17. Installing H2O

18. Import & Initialize

19. Pima Indians Diabetes Data
• This dataset is originally from the National Institute of Diabetes and
Digestive and Kidney Diseases.
• All patients here are females at least 21 years old of Pima Indian heritage.

20. Machine Learning Workflow

21. Dataset Description
Column Name Description
Pregnancies Number of times pregnant
Glucose Plasma glucose concentration 2 hours in an oral glucose tolerance test
Blood Pressure Diastolic blood pressure (mm Hg)
Skin Thickness Triceps skin fold thickness (mm)
Insulin 2-hour serum insulin (mu U/ml)
BMI Body mass index (weight in kg / (height in m)^2)
Diabetes Pedigree Function Diabetes pedigree function
Age Age in years
Outcome Presence of diabetes or not (0 or 1)

22. Exploratory Data Analysis

23. Exploratory Data Analysis

24. Correlation Heat Map

25. Data Preparation
• Data cleaning
• Data standardization
• Outlier analysis
• Missing value treatment
• Categorical encoding
• Cross validation

26. Modeling Algorithms in H2O
• Generalized Linear Models
• Naive Bayes Classifier
• Distributed Random Forest
• Gradient Boosting Machine
• XGBoost
• Deep Learning
• Stacked Ensembles

27. Code
Getting started with H2O

28. References
DZone AI - Machine Learning with H2O
H2O Documentation
GBM Tuning
AutoML Demo

29. Q&A

30. Thank You.!