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February 2019

Advanced Machine Learning Masterclass: Brisbane, 27–28 February 2019

February 27 @ 9:30 am - February 28 @ 5:00 pm
Saxons Training Facilities, Brisbane, Level 11, 300 Adelaide Street
Brisbane, QLD 4000 Australia
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This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. Topics covered will include data exploration, data preparation, feature engineering, and prediction, using advanced modelling techniques including glmnet, xgboost, and random forests. Participants…

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March 2019

Advanced Machine Learning Masterclass: Sydney, 5–6 March 2019

March 5 @ 9:30 am - March 6 @ 5:00 pm
City Desktop, Sydney, City Desktop, Level 4, 60 York Street
Sydney, NSW 2000 Australia
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This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. Topics covered will include data exploration, data preparation, feature engineering, and prediction, using advanced modelling techniques including glmnet, xgboost, and random forests. Participants…

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Advanced masterclass 2 – Random forests: Sydney, 26–27 March 2019

March 26 @ 9:30 am - March 27 @ 5:00 pm
City Desktop, Sydney, City Desktop, Level 4, 60 York Street
Sydney, NSW 2000 Australia
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This class will explore the many unique applications and extensions of the randomForest package, many of which are implemented in R. Access to these methods allows the user to easily solve problems not susceptible to other methods, including deep learning. Topics will include: A brief overview of the random forest algorithm. Out-of-sample estimates on training data, and applications in fraud, risk and outlier detection—random forests can make confident predictions on training data, unlike most other methods. Single-model quantile regression—estimating a…

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April 2019

Advanced Machine Learning Masterclass: Auckland, 4–5 April 2019

April 4 @ 9:30 am - April 5 @ 5:00 pm
Auldhouse, Auckland, 338 Ponsonby Rd
Ponsonby, Auckland 1011 New Zealand
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This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. Topics covered will include data exploration, data preparation, feature engineering, and prediction, using advanced modelling techniques including glmnet, xgboost, and random forests. Participants…

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Advanced masterclass 2 – Random forests: Melbourne, 29–30 April 2019

April 29 @ 9:30 am - April 30 @ 5:00 pm
Saxons Training Facilities, Melbourne, Saxons Training Facilities, Level 8, 500 Collins Street
Melbourne, Victoria 3000 Australia
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This class will explore the many unique applications and extensions of the randomForest package, many of which are implemented in R. Access to these methods allows the user to easily solve problems not susceptible to other methods, including deep learning. Topics will include: A brief overview of the random forest algorithm. Out-of-sample estimates on training data, and applications in fraud, risk and outlier detection—random forests can make confident predictions on training data, unlike most other methods. Single-model quantile regression—estimating a…

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