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Mallet is a Java-based system for processing text documents. Mallet can be used for machine learning applications to text. It includes sophisticated tools for document classification, including: (a) efficient routines for converting text to “features”, (b) a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and (c) code for evaluating classifier performance using several commonly used metrics. Mallet includes tools for sequence tagging for applications such as named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields. These methods are implemented in an extensible system for finite state transducers.
Simple machine learning program that uses hand-coded decisions about which way a star is pointing. Created by comedian Jim Gaffigan. Filmed in a single take and set in a parking lot, it opens with a driver waiting at the entrance of an office complex. He looks to see that the sign pointing towards the building is facing away from him, so he pulls into the parking lot and begins to follow the signs inside to find his office.
This course is designed for the patient who just got out of a job or wants to re-enter the workforce after being out for a long time. The course is self paced so it can be finished on your time. It will be a mix of learning the R language and working on our application. By the end of the course you will be ready to get to work in R.
Learn the basics of machine learning, apply it to real-world problems and take advantage of new and emerging libraries.
In this course, you will learn how to analyze your data, build a predictive model, evaluate how well it works, and finally deploy it to production.
Our goal is to help you understand the scientific foundation of machine learning, show you how it works, and ensure you are prepared for a career in data science.
Whether you are already an experienced data scientist, or want to get your hands dirty with machine learning, this course is for you.
By the end of the course you will know how to:
• Learn the basics of machine learning
• Build a predictive model
• Evaluate how well it works
• Deploy it to production
COURSE ASSIGNMENTS
There will be two assignments:
• Peer Evaluation
• Practical Project
COURSE OUTLINE
The course is self-paced. We will be meeting once every
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