Level: Intermediate to Advanced
Prerequisite: See below
David Langer
Expert in Machine Learning, Python, and R
Founder
Dave on Data
This course is a hands-on introduction to text analytics using Python. Attendees will learn the fundamentals of building pipelines that clean and transform text documents into formats that can be fed to clustering and classification machine learning algorithms.
Although this course contains some mathematics, the level of math is accessible to a broad audience and the focus is on concepts, not calculations.
You Will Learn
- Representing text documents using the bag of words model
- Tokenization
- Stopword removal
- Stemming and lemmatization
- Part-of-speech (POS) tagging
- N-grams
- Term frequency-inverse document frequency (TF-IDF)
- Grouping text documents based on similarity
- Classifying text documents
- Additional resources for honing skills
Geared To
- Business and data analysts
- BI and analytics developers and managers
- Business users
- Anyone interested in using text analytics with their business data
Prerequisites
Registrants must be familiar with Python and Python Notebooks or complete a complimentary online course before the conference. Access to the four-hour online course "Python Quick Start" will be provided to registrants three weeks prior to the event.
No background in advanced mathematics or statistics is required.
Laptop Setup
Students must bring their laptops to class.
Machine Requirements:
- Windows or Max OS X
- 64-bit operating system
- 8 GB available RAM, 16 GB preferred
- 4 GB of HD space for Anaconda Python installation
Anaconda Python is used in this course because it is free, easy to install, and has all the needed libraries.
Setup:
Laptop setup is required BEFORE the conference. Instructions will be emailed to registrants before the event.
There is no time allotted in class for laptop preparation.
* Enrollment is limited to 40 attendees.