Applied Machine Learning

“All models are wrong, But some are useful.” ― George Box

Every time we interact with an e-commerce site and see a recommendation to buy a product or we interact with our messenger app and see a chat bot in action, we are seeing machine learning in action. Strong mathematical theories underpin these machine learning application. And the Machine Learning library eco-system has matured to an extent that it is straight forward to write a few lines of code and have the ML back-end ready for one’s application.

However, the challenge for many beginners is how to structure a business problem as a ML problem, and then go on to build, select and evaluate the right model. This workshop is designed to help learn how to apply machine learning to business problems. Real-life case studies are used to teach the various algorithms and techniques. The focus will be on applications, rather than on exposition of the various algorithms.

Key Concepts

Approach

This would be a three-day instructor-led hands-on workshop to learn and implement an end-to-end machine learning models. This is predominantly a hands-on course and will be 70% programming/coding and 30% theory. There will be twelve sessions of two hours each over three days.

Session 1: Introduction & Concepts

Session 2: Model Building: Tree-based

Session 3: Model Validation & Selection

Session 4: Ensemble Models

Session 5: Building Model: Linear

Session 6: Feature Engineering

Session 7: Build & Deploy ML Service

Session 8: Interpret ML Models

Session 9: Dimensionality Reduction

Session 10: Clustering

Session 11: ML Challenges & Automation

Session 12: Practice Session & Wrap-up

Target Audience

Pre-requisites

Software Requirements

We will be using Python data stack for the workshop. Please install Ananconda for Python 3.5 for the workshop. That has everything we need for the workshop. For attendees more curious, we will be using Jupyter Notebook as our IDE. We will be using primarily scikit-learn libraries for most of the machine learning algorithms.

Facilitators’ Profile

Amit Kapoor teaches the craft of telling visual stories with data. He conducts workshops and trainings on Data Science in Python and R, as well as on Data Visualisation topics. His background is in strategy consulting having worked with AT Kearney in India, then with Booz & Company in Europe and more recently for startups in Bangalore. He did his B.Tech in Mechanical Engineering from IIT, Delhi and PGDM (MBA) from IIM, Ahmedabad. You can find more about him at http://amitkaps.com/ and tweet him at @amitkaps.

Bargava Subramanian is a practicing Data Scientist. He has 14 years of experience delivering business analytics solutions to Investment Banks, Entertainment Studios and High-Tech companies. He has given talks and conducted workshops on Data Science, Machine Learning, Deep Learning and Optimization in Python and R. He has a Masters in Statistics from University of Maryland, College Park, USA. He is an ardent NBA fan. You can tweet to him at @bargava.