All Projects → GJena → Nlp Tutorial

GJena / Nlp Tutorial

Tutorial for Natural Language Processing

Projects that are alternatives of or similar to Nlp Tutorial

Tabgen
Tablature generation system
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Bioinformatics
Course materials for Computational Biology and Bioinformatics - BI462
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Ml unsam
Repositorio de la materia Aprendizaje Automático (2do cuatrimestre de 2020)
Stars: ✭ 18 (+0%)
Mutual labels:  jupyter-notebook
Simplest Tensorflow Tensorboard Mnist Embedding Visualisation
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Ilearndeeplearning.py
This repository contains small projects related to Neural Networks and Deep Learning in general. Subjects are closely linekd with articles I publish on Medium. I encourage you both to read as well as to check how the code works in the action.
Stars: ✭ 896 (+4877.78%)
Mutual labels:  jupyter-notebook
Dlsm
Deep Learning Study Material
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Foundations course
Materials for the preparatory course for new students of the Master of Autonomous Systems program
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Symptoms Tracker
A scalable web app for tracking covid-19-related symptoms amongst confined populations
Stars: ✭ 18 (+0%)
Mutual labels:  jupyter-notebook
Computer Vision
Computer vision exercise with Python and OpenCV.
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Tensorflowmnist
Various neural networks on MNIST data using TensorFlow library
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Uber profit model
A predictive model to help Uber drivers make more money
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Ansible Jupyter.dockerfile
Building the Docker image with Ansible and Jupyter.
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Algorithms
Repository for the Algorithms course
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Pulmonary nodules ai diagnosis
Tianchi medical AI competition [Season 1]: Intelligent diagnosis of pulmonary nodules. 肺部结节智能诊断
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
E Healthify
App developed for Hack-the-Crisis India.
Stars: ✭ 18 (+0%)
Mutual labels:  jupyter-notebook
Pydhamed
Dynamic Histogram Analysis To Determine Free Energies and Rates from Biased Simulations
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Lab files
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook
Jupyter Text2code
A proof-of-concept jupyter extension which converts english queries into relevant python code
Stars: ✭ 897 (+4883.33%)
Mutual labels:  jupyter-notebook
Python fundamentals
Python fundamentals is a series of tutorials on Basic Python knowledge required to get started in the field of Data Science.
Stars: ✭ 18 (+0%)
Mutual labels:  jupyter-notebook
National generation capacity
Data package: national generation capacity
Stars: ✭ 17 (-5.56%)
Mutual labels:  jupyter-notebook

Tutorial: Natural Language Processing

Instructor: Grishma Jena

How can computers interpret something so human like language? Can they actually understand what we are saying or are they hiding behind a façade of rules and algorithms? How do these systems of zeroes and ones make sense of words? This workshop introduces Natural Language Processing in Python and sheds light on how computers interpret our language. Attendees are introduced to NLTK and Gensim that help them tokenise, process and represent textual data. We will see how data is distilled into different linguistic features that power Machine Learning applications like text classifier, sentiment analyser and topic modeler.

We will be using Jupyter to execute Python code for the purpose of this Natural Language Processing tutorial. It is highly recommended to use Python 3 as Python 2 will be has been sunset on January 1, 2020. A virtual environment can be used to manage and isolate the packages for our project. Please follow these instructions to have all the dependencies ready before the tutorial as that will enable us to hit the ground running.

Pre-requisites

Using Jupyter on your local machine

Requires installation of packages but you will be able to use Jupyter and run code offline.

  1. Ensure that pip is installed and upgrade it. Pip should already be available if you are using Python 2 >= 2.7.9 or Python 3 >= 3.4 downloaded from python.org. For further installation instructions check this.

  2. Optional: If you plan on using a virtual environment, ensure virtualenv (Python 2) or venv (Python 3) is installed. Create a virtual environment and activate it. Detailed instructions here.

  3. Install the required packages using pip in the terminal:

  • Python 3:
python3 -m pip install jupyter nltk lxml requests matplotlib sklearn
wikipedia gensim wordcloud --user
  • Python 2:
python -m pip install jupyter nltk lxml requests matplotlib sklearn
wikipedia gensim wordcloud --user

If you face problems installing NLTK, take a look at this or try with Python 3.

  1. Open a Jupyter notebook with jupyter notebook in your terminal. This opens in your browser at default port 8888.

  2. Download the sample notebook ‘Introduction to NLP test’ and open it in Jupyter. Execute the code by clicking on Cell -> Run Cells. Check out this video for a quick introduction to Jupyter.

Feel free to contact me in case of any queries.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].