Demystifying Machine Learning

Learn about cloud based machine learning algorithms using AWS

In this one-day workshop, you will learn cloud-based machine learning (ML) solutions on the AWS platform. You will learn about the basics of Machine Learning, few of the most commonly used algorithms, and finally we will do some small projects using Amazon SageMaker. You will gain hands-on model development experience on few of the popular machine learning algorithms like XGBoost and Time Series Forecasting, using Recurrent Neural Networks(RNN).


Mix of class-room training and hands-on workshop (yes, do bring your laptops). The timings are as below:

09.30am - 10.00am: Registration cum installation & setup

10.00am - 11.30pm: Session

11.30am - 11.45am: Tea break

11.45am - 01.00pm: Session

01.00pm - 02.00pm: Lunch

02.00pm - 03.30pm: Session

03.30pm - 03.45pm: Tea break

03.45pm - 05.45pm: Session

05.45pm - 06.00pm: Wrap-up / closure


This course is intended for:

  • Anyone who is passionate about Machine Learning, and wondering how to get started
  • If you are new to machine learning, this might be a good starting point
  • Developers who want to understand concepts behind machine learning and how to implement a machine learning solution on AWS


In this course, we will cover:

  • Introduction to Machine Learning and its core concepts 
  • Feature Selection and Feature Engineering 
  • Model Performance and Optimization 
  • Introduction to Amazon SageMaker
  • Introduction to XGBoost and Gradient Boosted Trees
  • Introduction to DeepAR Time Series Forecasting
  • Hands on Lab– using Kaggle Dataset with Amazon Sagemaker


We recommend that attendees of this course have the following prerequisites:

  • Basic understanding of machine learning processes
  • Basic knowledge of Python


This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises.


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Suman Debnath, Principal Developer Evengalist, AWS

Suman Debnath is a Principal Developer Advocate at Amazon Web Services. He is extremely passionate about Big Data, Machine Learning and Deep Learning. Prior to joining AWS, he working at various organisations like IBM Software Lab, EMC, NetApp and Toshiba where he worked with System and Performance Engineering teams, focusing on storage protocols(FC, NVMeoF), performance benchmarking and tool development.