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, dimensionality reduction. Dimensionality reduction serves two main purposes. First, it reduces the computational complexity of working with very large datasets. Second, it removes the non-relevant information in a dataset, surfacing the information that matters most. We will use dimensionality reduction algorithms to build an anomaly detection system; specifically, we will build a system to detect credit card fraud without using any labels. Anomaly detection systems are widely used in industry today to detect all types of rare events such as fraud (e.g., credit card, wire, cyber, insurance), crime (e.g., hacking, money laundering, drug, arms, and human trafficking), and adverse events (e.g., financial market meltdowns, cardiac events, and spikes in online traffic).

DIFFICULTY LEVEL: INTERMEDIATE

Instructor Bio:

Ankur Patel

Co-founder and Head of Data | Glean

Ankur Patel

Ankur Patel is the co-founder & Head of Data at Glean, an AI-powered spend intelligence solution for managing vendor spend, and the co-founder of Mellow, a fully managed machine learning platform for SMBs. He 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. Prior to founding Glean and Mellow, Ankur led data science and machine learning teams at several startups including 7Park Data, ThetaRay, and R-Squared Macro and was the lead emerging markets trader at Bridgewater Associates. He is a graduate of Princeton University and currently resides in New York City.

Course Outline

Module 1: Introduction to Unsupervised Learning

  • How unsupervised learning fits into the machine learning ecosystem
  • Common problems in machine learning: insufficient labeled data, curse of dimensionality, and outliers

Module 2: Introduction to Dimensionality Reduction

  • Motivation for dimensionality reduction: reduce computational complexity of large data, remove non-relevant information and surface salient information, perform anomaly detection, perform clustering
  • Linear Dimensionality Reduction Algos
  • Non-linear Dimensionality Reduction Algos

Module 3: Application: Anomaly Detection

  • Introduce use case: credit card fraud detection
  • Explore and prepare the data
  • Define evaluation function
  • Apply linear dimensionality reduction and evaluate results
  • Apply non-linear dimensionality reduction 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.