Converting words to their base forms using stemming.
Converting words to their base forms using lemmatization.
Dividing text data into chunks.
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.
This course is a part of a series of courses specialized in artificial intelligence :
Understand and Practice AI - (NLP, Recommendation System, Speech Recognition, Computer Vision, OpenCV, Machine Learning, Supervised Learning, Unsupervised Learning, Artificial Neural Network, Reinforcement Learning, Deep Learning, Building Games with AI, Genetic Algorithms)
This course is focusing on 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 and how 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 world projects such as :
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
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