NLTK: Build Document Classifier & Spell Checker with Python
What you'll learn
- NLTK Main Functions: Concordance, Similar, Lexical Dispersion Plot
- Text Tokenization
- Text Normalization: Stemming & Lemmatization
- Text Tagging: Unigram, N-Gram, Regex
- Text Classification
- Project 1: Gender Prediction Application
- Project 2: Document Classification Application
- Information Extraction from Text: Chunking, Chinking, Name Entity Recognition
- Source Code *.py Files of All Lectures
- English Captions for All Lectures
- Q&A board to send your questions and get them answered quickly
Course content
- Preview05:44
- 01:04Course Technical Requirements
- Preview02:56
- Preview04:26
- Preview10:28
- 01:02Summary: NLTK Basic Functions
- 07:50Frequency Distribution with NLTK
- 04:39Frequency Distribution on Your Text with NLTK
Requirements
- Good Python level. This Natural Language Processing (NLP) tutorial assumes that you already familiar with the basics of writing simple Python programs and that you are generally familiar with Python's core features (data structures, file handling, functions, classes, modules, common library modules, etc.).
- Python 3.4+ (or 2.7). Please note that the tutorial codes are written in Python 3, but it is up to you to fine-tune them if you want to run them on Python 2.
Description
This Natural Language Processing (NLP) tutorial covers core basics of NLP using the well-known Python package Natural Language Toolkit (NLTK). The course helps trainees become familiar with common concepts like tokens, tokenization, stemming, lemmatization, and using regex for tokenization or for stemming. It discusses classification, tagging, normalization of our input or raw text. It also covers some machine learning algorithms such as Naive Bayes.
After taking this course, you will be familiar with the basic terminologies and concepts of Natural Language Processing (NLP) and you should be able to develop NLP applications using the knowledge you gained in this course.
What is Natural Language Processing (NLP)?
Natural language processing, or NLP for short, is the ability of a computer program to understand, manipulate, analyze, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, topic segmentation, and spam detection.
What is NLTK?
The Natural Language Toolkit (NLTK) is a suite of program modules and data-sets for text analysis, covering symbolic and statistical Natural Language Processing (NLP). NLTK is written in Python. Over the past few years, NLTK has become popular in teaching and research.
NLTK includes capabilities for tokenizing, parsing, and identifying named entities as well as many more features.
This Natural Language Processing (NLP) tutorial mainly cover NLTK modules.
About the course
This Natural Language Processing (NLP) tutorial is basically designed to make you understand the fundamental concepts of Natural Language Processing (NLP) with Python, and we will be learning some machine learning algorithms as well because natural language processing and machine learning move hand in hand as NLP employs machine learning techniques to learn and understand what a sentence is saying, or what a user has said and it sends an appropriate response back.
So, by the end of this course, I hope you will have a clear idea, a clear view of the core fundamental concepts of NLP and how we can actually make applications using these core concepts.
Looking forward to seeing you in the course.
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Keywords: Natural Language Processing (NLP) tutorial; Python NLTK; Machine Learning; Sentiment Analysis; Data Mining; Text Analysis; Text Processing
Who this course is for:
- This Natural Language Processing (NLP) tutorial is desined for Python programmers who want to learn more about Natural Language Toolkit (NLTK).
Instructors
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