https://github.com/omarsar/text_mining_lab_2017
Requirements for Text Mining Summer Course (Lab Session)
https://github.com/omarsar/text_mining_lab_2017
ai data-minig data-science deep-nlp machine-learning nlp text-mining word2vec
Last synced: 6 months ago
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Requirements for Text Mining Summer Course (Lab Session)
- Host: GitHub
- URL: https://github.com/omarsar/text_mining_lab_2017
- Owner: omarsar
- Created: 2017-06-26T05:30:52.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-07-05T02:53:41.000Z (over 8 years ago)
- Last Synced: 2025-03-24T18:13:09.211Z (7 months ago)
- Topics: ai, data-minig, data-science, deep-nlp, machine-learning, nlp, text-mining, word2vec
- Language: Jupyter Notebook
- Size: 14.3 MB
- Stars: 4
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Hello Everyone,
Here is the list of packages needed for our Text Mining Lab Session scheduled for 6/29/2017 (2:00-5:00 p.m.)
#### Updates:
------------------
* I have uploaded some poster examples of some past students. (Check the `posters` folder)
* For the guys intereted in the slack community, send me your email to `ellfae@gmail` and I will provide an invite
* If you have any other questions or technical problems, feel free to stop by Idea Lab Delta 701. I will be more than happy to assist.
* I may extend the python notebook based on the excellent questions you guys asked (e.g., more statistics, visuals, etc.)
* Lastly, good luck and enjoy your stay here.
#### Software:
------------------
* Python 3 (coding will be done strictly using Python 3)
* Anaconda Environment (recommended but not mandatory) (https://www.continuum.io/downloads)
* Jupyter (http://jupyter.org/)
* Google's word2vec (Download the file... warning! it is really huge)(https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing)
* Gensim (https://radimrehurek.com/gensim/)
* Scikit Learn (http://scikit-learn.org/stable/) (get the latest version)
* Pandas (http://pandas.pydata.org/)
* Matplotlib (https://matplotlib.org/)
* NLTK (for stopwords) (http://www.nltk.org/)
#### Computing Resources:
-------------------
* Operating System: Preferably Linux or MacOS (Windows break but you can try it out)
* RAM: 4GB
* Disk Space: 8GB (mostly to store word embeddings)
#### Test:
-------------------
Once you have installed all the necessary packages, you can test to see if everything is working by running the following python code:
```python
import logging
logging.root.handlers = [] # Jupyter messes up logging so needs a reset
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
from smart_open import smart_open
import pandas as pd
import numpy as np
from numpy import random
import gensim
import nltk
from sklearn.cross_validation import train_test_split
from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.metrics import accuracy_score, confusion_matrix
import matplotlib.pyplot as plt
from gensim.models import Word2Vec
from sklearn.neighbors import KNeighborsClassifier
from sklearn import linear_model
from nltk.corpus import stopwords
%matplotlib inline
```
If you have any further questions please feel free to contact me at ellfae@gmail.com
Have Fun,
Elvis Saravia (Text Mining TA)