Highlight of the Week - Unsupervised Learning 1: Intro to Unsupervised Learning, Dimensionality Reduction, and Anomaly Detection
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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
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.
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Python coding experience
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Familiarity with pandas, numpy, and scikit-learn
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Understanding of basic machine learning concepts, including supervised learning
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Experience with deep learning and frameworks such as TensorFlow or PyTorch is a plus