Homework курс

Homework курс

Homework 1 Курс _Windows администрация_

Not satisfied with the tutor? Yelp has found that Naive Bayes is more effective than Mechanical Turks at categorizing businesses. If you have any problems opening the files, you probably need to turn off real-time virus scanning especially Microsoft Security Essentials. How it Works 4 minutes for a brief overview of the Kaggle platform. Automatically Categorizing Yelp Businesses discusses how Yelp uses NLP and scikit-learn to solve the problem of uncategorized businesses.

Come up with some theories about which features might be relevant to predicting the response, and then explore the data to see if those theories appear to be true. If it’s not installed, run pip install textblob at the command line not from within Python. WriteLine “Please enter the amount of Euro you wish to be converted: Please submit a link to your project repository with paper, code, data, and visualizations before class.

Andrew Ng has a paper comparing the performance of logistic regression and Naive Bayes across a variety of datasets. If you liked your first lesson, you can continue learning by choosing a suitable package of lesson hours. We will be using Python for all programming assignments and projects. Reload to refresh your session. DC Natural Language Processing is an active Meetup group in our local area. This course introduces methods for five key facets of an investigation: Ask for a free lesson with another tutor of your choice.

Identifying Humorous Cartoon Captions is a readable paper about identifying funny captions submitted to the New Yorker Caption Contest. If you want to get serious about NLP, Stanford CoreNLP is a suite of tools written in Java that is highly regarded. These slides from the University of Maryland provide more mathematical details on both logistic regression and Naive Bayes, and also explain how Naive Bayes is actually a “special case” of logistic regression.

Modern Methods for Sentiment Analysis shows how “word vectors” can be used for more accurate sentiment analysis. This slide deck defines many of the key NLP terms. This notebook compares their performances on such a dataset. The course is also listed as AC, STAT, and E All lectures will be posted here and should be available 24 hours after meeting time.

Or, just read through the slides. When working with a large text corpus in scikit-learn, HashingVectorizer is a useful alternative to CountVectorizer. Confirm that you have TextBlob installed by running import textblob from within your preferred Python environment.

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