What you'll learn:
- Understand every detail and build real stuff in NLP
- (NEW)Learn how some plugins use semantic search to generate source code
- (NEW)Building your vocabulary for any NLP model
- (NEW)Reducing Dimensions of your Vocabulary for Machine Learning Models
- (NEW)Feature Engineering and convert text to numerical values for machine learning models
- (NEW) Keyword search VS Semantic search
- (NEW)Similarity between documents
- (NEW)Dealing with WordNet
- (NEW)Search engines under the hood
- Tokenizing text data
- Converting words to their base forms using stemming
- Converting words to their base forms using lemmatization
- Dividing text data into chunks
- Dealing with corpuses
- Extracting document term matrix using the Bag of Words model
- Building a category predictor
- Constructing a gender identifier
- Building a sentiment analyzer
- Topic modeling using Latent Dirichlet Allocation
-- UPDATED -- (NEWLESSONSARENOTINTHEPROMOVIDEO)
THISCOURSEISFORBEGINERSORINTERMEDIATES, ITISNOTFOREXPERTS
This course is a part of a series of courses specialized in artificial intelligence :
Understand and Practice AI - (NLP)
This course is focusing on the NLP:
Learn key NLP concepts and intuition training to get you quickly up to speed with all things NLP.
I will give you the information in an optimal way, I will explain in the first video for example what is the concept, and why is it important, what is the problem that led to thinking about this concept andhow can I use it (Understand the concept). In the next video, you will go to practice in a real-world project or in a simple problem using python (Practice).
The first thing you will see in the video is the input and the output of the practical section so you can understand everything and you can get a clear picture!
You will have all the resources at the end of this course, the full code,and some other useful links and articles.
In this course, we are going to learn about natural language processing. We will discuss various concepts such as tokenization, stemming, and lemmatization to process text. We will then discuss how to build a Bag of Words model and use it to classify text. We will see how to use machine learning to analyze the sentiment of a given sentence. We will then discuss topic modeling and implement a system to identify topics in a given document. We will start with simple problems in NLP such as Tokenization Text, Stemming, Lemmatization, Chunks, Bag of Words model. and we will build some real stuff such as :
Learning How to Represent the Meaning of Natural Language Text
Building a category predictor to predict the category of a given text document.
Constructing a gender identifier based on the name.
Building a sentiment analyzer used to determine whether a movie review is positive or negative.
Topic modeling using Latent Dirichlet Allocation
Feature Engineering
Dealing with corpora and WordNet
Dealing With your Vocabulary for any NLPand MLmodel
TIPS (for getting through the course):
Take handwritten notes. This will drastically increase your ability to retain the information.
Ask lots of questions on the discussion board. The more the better!
Realize that most exercises will take you days or weeks to complete.
Write code yourself, don’t just sit there and look at my code.
You don't know anything about NLP? let's break it down!
I am always available to answer your questions and help you along your data science journey. See you in class!
NOTICEthat This course will be modified and I will addnew content and new conceptsfrom one time to another, so stay informed! :)
