Automated Machine Learning (AutoML) provides methods and processes to automate the end-to-end machine learning workflow towards solving real-world business problems.

In traditional machine learning world, data scientist used to spend a considerable amount of time in Data wrangling, model selection and tuning, now with the advances of AutoML it provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.

Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand.

 
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Outline/Structure of the Talk

1. Machine and Deep Learning Introduction

2. Why Automated Machine learning

3. Auto ML libraries (H2O, Auto-Keras, T-POT) and algorithms in R and Python

4. Industry usage and integration with Cloud (GCP)

5. Auto ML demo using Banking industry case study in R

Learning Outcome

Participants will take back good fundamental concepts of Deep Learning and Automated Machine Learning using different libraries in R and Python.

Beginners to Machine learning will learn how to develop model in 15 mins and experts in ML will learn about how to leverage Auto ML to develop model for complex use cases.

Cloud integration using GCP will also demoed.

Target Audience

Machine Learning enthusiast, Beginners and Data scientist

Prerequisites for Attendees

1. Basics of Machine Learning and Deep Learning

2. Supervised and Unsupervised Learning

3. Cloud (GCP, AWS) knowledge - Good to have (not must to have)

schedule Submitted 2 weeks ago

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