Course Abstract

Training duration: 90 min (Hands-on)

In this 90-minute course, Ankur Patel will explore one of the core concepts in unsupervised learning, clustering. Clustering is able to segment entities (e.g., users) into distinct and homogenous groups such that members of a group are very similar to members within the group but distinctly different from members in other groups. This group segmentation is possible without requiring any labels whatsoever and instead relies on separating entities based on behavior. For example, via clustering, online shoppers could be grouped into budget-conscious shoppers, high-end shoppers, frequent shoppers, seasonal shoppers, technophiles, audiophiles, sneakerheads, back-to-school shoppers, young parents, senior citizens, and millennials. To perform clustering well, good feature engineering is required. In this course, we will explore loan applications, perform feature engineering, and segment users based on their potential creditworthiness. We will also explore how clustering allows efficient labeling, turning unlabeled problems into labeled ones, opening up the realm of semi-supervised learning.


Instructor Bio:

Ankur Patel

Co-founder and Head of Data | Glean

Ankur Patel

Ankur Patel is the co-founder and Head of Data at Glean. Glean uses NLP to extract data from invoices and generate vendor spend intelligence for clients. Ankur is an applied machine learning specialist in both unsupervised learning and natural language processing, and he is the author of Hands-on Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data and Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand. Previously, Ankur led teams at 7Park Data, ThetaRay, and R-Squared Macro and began his career at Bridgewater Associates and J.P. Morgan. He is a graduate of Princeton University and currently resides in New York City.

Course Outline

Module 1: Introduction to Clustering

  • Why the need for clustering is exists / the real world motivation
  • How to find patterns in data with zero or few labels
  • How to efficiently label data when only few labels are available

Module 2: Overview of Clustering Algorithms

  • K-Means
  • Hierarchical clustering
  • Apply to MNIST and Fashion MNIST datasets
  • Visualize clusters and evaluate results

Module 3: Application: Group Segmentation

  • Introduce use case: loan applications
  • Explore and prepare the data
  • Define evaluation function
  • Apply clustering algorithms and evaluate results

Background knowledge

  • Python coding experience

  • Familiarity with pandas, numpy, and scikit-learn

  • Understanding of basic machine learning concepts, including supervised learning

  • Experience with deep learning and frameworks such as TensorFlow or PyTorch is a plus

Applicable Use-cases

  • Fraud Detection: Identify fraud in transactional data such as credit card, ACH, wire, and insurance claims

  • Anti-money Laundering: Detect potential money laundering for banks.

  • Cybersecurity: Stop malicious activity such as hacking

  • Machine Maintenance: Monitor sensor data to detect when machines are starting to malfunction

  • Disease Diagnosis: Spot potential disease using healthcare IoT sensor data.