You. Hello and welcome to text mining and.
Natural language processing for research and analysis.
I'm Michael Wang and I'll be taking.
You through this course.
A little bit about myself.
I'm the principal and the AI consultant at Ypred.
I'm the head of data science at a fintech
company in Australia, and I have 13 years experience consulting
to various banks and investment consultancies.
Also in investing, teaching AI and data science.
Throughout this course, I will be teaching.
You some of the things I've learned.
Throughout my career, as well as some.
Of the core techniques behind using natural.
Language processing and text mining for research analysis.
So the course is structured as ten.
Modules, and in each module you'll learn.
A core part of the curriculum.
We will start with an intro to.
Text mining, natural language processing and its.
Use cases, and how it has evolved over time.
And next we'll move on to text.
Preprocessing and also introduction to Python programming.
Python will be the core programming language.
We'Ll be using throughout the course, and.
With the learnings that we pick up.
In natural language processing and text mining.
We'Ll be applying it with hands on.
Python exercises and case studies.
In module three, we'll move on to.
Feature extraction and embeddings, looking at turning.
Words into usable data, numeric data that.
Can be used for models for tasks.
Further down in the natural language processing pipeline.
And some of those tasks will include.
Machine learning, which we will learn in module four.
Moving on.
We'll move on to unsupervised learning and.
Clustering in module five, followed by advanced language processing
techniques in module six, such as parts of speech tagging, name entity.
Recognition, topper mining, and sentiment analysis.
After that, we'll move on into more
advanced models, and these include deep learning.
Models such as rnns, lstms, and transformers.
Finally, in modules nine and ten, we'll.
Discuss large language models, Chat GPT, and how to effectively use Chat GPT, in.
Addition to how to evaluate some of.
The techniques that we learned and finishing.
Off the discussion on model interpretability and a framework called
pass in approaching natural language and text mining tasks.
I'm excited for you to join me.
And I hope to see you soon.