3.95 (150 reviews)
☑ Concepts and practical applications of Natural Language Processing using Python and NLTK.
Natural Language Processing (NLP) is a hot topic into the Machine Learning field.
This course is focused in practical approach with many examples and developing functional applications. This course starts explaining you, how to get the basic tools for coding and also making a review of the main machine learning concepts and algorithms. After that this course offers you a complete explanation of the main tools in NLP such as: Text Data Assemble, Text Data Preprocessing, Text Data Visualization, Model Building and finally developing NLP applications.
Hot topics on NLP that I will cover with practical applications on this course are:
- Regular expressions - Scrapping the web
- Textract library for extracting text content
- Sentence splitter and tokenization
- Stemming and Lemmatization
- Stop and rare word removal
- Part of Speech (POS) tagging
- Bag of Words: TfidfVectorizer
- Frequency Chart
- Co-occurence matrix
- Word cloud library
- Text similarity
- Text clustering
- Latent Semantic Analysis
- Topic Modeling
- Text Classification
- Sentiment Analysis
- Word2Vec library
- Recommender Systems: Collaborative Filtering
- Spam detector app
- Social Media Mining on Twitter
and much more!...
In this course you will find a concise review of the theory with graphical explanations and for coding it uses Python language and NLTK library.
Finally this course offers you many datasets and other resources for your practice and study.
The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: firstname.lastname@example.org or by Twitter: @AILearningCQ
Environment Setup and Installation
Introduction to the course NLP with Python and NLTK
Before starting this course read this Guidelines
Environment Setup: Anaconda for Windows
VirtualBox and Ubuntu
Environment Setup: Anaconda for Linux
Codes in Python 3.6 and Installation Instructions
Machine Learning Review
Machine Learning concepts: Overfitting, Underfitting and Cross Validation
Classification model and evaluation: Confusion matrix and ROC curve
Support Vector Machines
Decision Trees and Random Forest
Collecting Text Data
Regular Expressions - part 1
Regular Expressions - part 2
Extracting text content from different media with Textract
Text Data Preprocessing
Sentence splitter and Tokenization
Stemming and Lemmatization
Stop word removal
Part of Speech (POS) tagging
Bag of Words: CountVectorizer, TfidfVectorizer
Text Data Visualization
Latent Semantic Analysis
Recommender Systems : Collaborative Filtering part 1
Recommender Systems : Collaborative Filtering part 2
Text Summarization app
Spam detector app
Word2Vec on Wikipedia
Recommender System with KNN
Social Media Mining on Twitter
The End Class
Great NLP, Python and NLTK introductory course. This course has lots of simple concept and insightful knowledge, and is easy to learn.
This course gives all basic information about NLP. However, the course is really suitable for the beginners, who wants to take a step into world of NLP
Excellent course. Straight to the point, clear and very relevant topics. Best NLP course that I have taken yet.
i took the time to analized the data and is really helpfull sorry most of the curse is an advertisement of the others curses
Good course to set your feet in NLP. Really helped me a lot in creating business solutions for my clients. Thank you.
In the beginning I thought the course was not helping me much, but later on I reviewed some of the key concepts again by watching a few videos repeatedly. I was able to understand the concepts better. I was able to identify some concepts for which I could find some useful applications. I feel the foundations of Word2Vec could have been better explained. Nevertheless what is shown here is quite useful.
Lot of concepts of libraries are not explained in Practical terms. Compare to other NLP course, this course is aligned in coverage of topics plus more examples covered but does not take to Expert zone where one can do exercises on advance NLP. For basic or beginner level this course is good!
I need to work in Python 3 not Python 2. When I purchased the course I tried to make sure I was only getting python 3 but it seems that is not the case here. While the theory will still be useful the syntax and how to will be deprecated and not useful.
good course , but need some improvment in chunking and manual feature extraction usng chunking and building models from that features ... use python 3 and try to add sentiment analyzer using deeplearning .. you can improve it also by adding Aspect based seentiment anlaysis .. using chunking fetaure extraction then clustering ... best reagrds
Great overview with many python code examples of using natural language process, especially NLTK. Very knowledgeable instructor.
Really well organized. Wished the author had taken one example with a two or three large text documents, but still I think this is a great starting point to understand text mining
Overall, I found this course to be an essential supplemental resources for a NLP project I am working on. I tried different courses within the Udemy site and this one was by far the most complete and useful. I would only suggest to have the code in Python 3 or to make sure that the code is also compatible to non-linux environments, but this is a big issue since to learn DS one needs to get dirty with the code.
Good intro to setup the workspace. Refresher of the classification methods. Videos are of good length. Screen quality can be improved/hard to see/read the text sometimes. Overall great course. Good job!!!