Waqar Ahmed
NLTK: Build Document Classifier & Spell Checker with Python
About This Course
NLP with Python - Analyzing Text with the Natural Language Toolkit (NLTK) - Natural Language Processing (NLP) Tutorial
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
NLTK Main Functions: Concordance, Similar, Lexical Dispersion Plot
Text Tokenization
Text Normalization: Stemming & Lemmatization
Course Curriculum
8 Lectures
5 Lectures
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6 Lectures
6 Lectures
6 Lectures
1 Lectures
4 Lectures
2 Lectures
Instructors
Instructors
GoTrained Academy
GoTrained is an e-learning academy aiming at creating useful content in different languages and it concentrates on technology and management. We adopt a special approach for selecting content we provide; we mainly focus on skills that are frequently requested by clients and jobs while there are only few videos that cover them. We also try to build video series to...
More Courses By Waqar Ahmed, GoTrained Academy, , Faizan Ali
Review
Peter M.
Wasn't sure how good this was going to be after the first 3 resources wouldn't load however, wow, did it get better!!
Essentially, one of the best training courses I've done so far. It brings together, python, regex, machine learning & more. It has allowed me to complete a project that I have been working on for some time by giving me the missing pieces.
Thank you and highly recommended!
Peter
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Sviatlana K.
The course is recorded by an Indian guy with an accent. Lessons are short, pretty simple.
Maybe lessons will be OK for the beginners, but I expected more.
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Shambhavi S.
not great
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Roy A.
The videos are easy to follow and mostly well-explained. The code is useful and clear. There are a few mistakes here and there, though, and most of the content is based on the Bird et al. (2009) NLP textbook rather than original material.
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Anthony D.
Overall this was an effective and succinct course. Some of the example files do not match the intuition or discussion. While not a big deal, you will need to follow along and not rely upon the example files. If you do, you will most likely miss something in the discussion. Some of the code also needs tuning, syntax (typos). Fixing the small issues is a great exercise if you want to learn. If you want to passively watch and not participate, by writing the code along side the discussion, you will definitely miss some great learning opportunities.
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Alejandro G. G.
Perfect for those that are starting NLP from scratch, the content is complete, and is very well explained.
Missing some more complete python/jupyter files, btw.
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Douglas K.
This course was very well thought out. It helped me understand all the things I struggled to understand via other articles or books. It's a good intro, and you should definitely checkout more resources after this, but I strongly recommend this as an intro
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Ignacio S. G.
Nice to have an overview of the basic concepts
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Prasanna A.
Discusses the underlying concepts but never explains on how these concepts tie to the big picutere. Always take a problem and explain how the concepts solve the problems.
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