WEBVTT

00:00:00.320 --> 00:00:03.860
You.
Hello and welcome to text mining and.

00:00:03.890 --> 00:00:07.900
Natural language processing
for research and analysis.

00:00:07.930 --> 00:00:10.170
I'm Michael Wang and I'll be taking.

00:00:10.200 --> 00:00:11.780
You through this course.

00:00:11.810 --> 00:00:13.500
A little bit about myself.

00:00:13.530 --> 00:00:17.300
I'm the principal and the AI
consultant at Ypred.

00:00:17.330 --> 00:00:19.500
I'm the head of data science at a fintech

00:00:19.530 --> 00:00:23.820
company in Australia,
and I have 13 years experience consulting

00:00:23.850 --> 00:00:27.900
to various banks
and investment consultancies.

00:00:27.920 --> 00:00:33.420
Also in investing,
teaching AI and data science.

00:00:33.450 --> 00:00:36.060
Throughout this course,
I will be teaching.

00:00:36.090 --> 00:00:38.340
You some of the things I've learned.

00:00:38.370 --> 00:00:40.920
Throughout my career, as well as some.

00:00:40.950 --> 00:00:43.460
Of the core techniques
behind using natural.

00:00:43.490 --> 00:00:48.660
Language processing and text
mining for research analysis.

00:00:48.680 --> 00:00:51.700
So the course is structured as ten.

00:00:51.730 --> 00:00:55.020
Modules, and in each module you'll learn.

00:00:55.050 --> 00:00:57.420
A core part of the curriculum.

00:00:57.450 --> 00:00:59.460
We will start with an intro to.

00:00:59.490 --> 00:01:02.340
Text mining, natural
language processing and its.

00:01:02.370 --> 00:01:05.700
Use cases, and how it
has evolved over time.

00:01:05.730 --> 00:01:08.290
And next we'll move on to text.

00:01:08.320 --> 00:01:12.850
Preprocessing and also
introduction to Python programming.

00:01:12.880 --> 00:01:15.980
Python will be the core
programming language.

00:01:16.010 --> 00:01:18.850
We'Ll be using throughout the course, and.

00:01:18.880 --> 00:01:20.930
With the learnings that we pick up.

00:01:20.960 --> 00:01:23.540
In natural language
processing and text mining.

00:01:23.570 --> 00:01:25.820
We'Ll be applying it with hands on.

00:01:25.850 --> 00:01:29.380
Python exercises and case studies.

00:01:29.410 --> 00:01:31.460
In module three, we'll move on to.

00:01:31.490 --> 00:01:35.210
Feature extraction and embeddings,
looking at turning.

00:01:35.240 --> 00:01:39.960
Words into usable data, numeric data that.

00:01:39.990 --> 00:01:42.740
Can be used for models for tasks.

00:01:42.770 --> 00:01:45.820
Further down in the natural
language processing pipeline.

00:01:45.850 --> 00:01:48.740
And some of those tasks will include.

00:01:48.770 --> 00:01:53.260
Machine learning, which we
will learn in module four.

00:01:53.290 --> 00:01:54.140
Moving on.

00:01:54.170 --> 00:01:56.400
We'll move on to unsupervised
learning and.

00:01:56.430 --> 00:02:01.100
Clustering in module five,
followed by advanced language processing

00:02:01.130 --> 00:02:06.850
techniques in module six, such as
parts of speech tagging, name entity.

00:02:06.880 --> 00:02:10.450
Recognition, topper mining,
and sentiment analysis.

00:02:10.480 --> 00:02:12.540
After that, we'll move on into more

00:02:12.570 --> 00:02:15.780
advanced models,
and these include deep learning.

00:02:15.810 --> 00:02:20.450
Models such as rnns,
lstms, and transformers.

00:02:20.480 --> 00:02:24.220
Finally, in modules nine and ten, we'll.

00:02:24.250 --> 00:02:32.300
Discuss large language models, Chat GPT,
and how to effectively use Chat GPT, in.

00:02:32.330 --> 00:02:34.780
Addition to how to evaluate some of.

00:02:34.810 --> 00:02:38.170
The techniques that we
learned and finishing.

00:02:38.200 --> 00:02:42.890
Off the discussion on model
interpretability and a framework called

00:02:42.920 --> 00:02:48.820
pass in approaching natural
language and text mining tasks.

00:02:48.850 --> 00:02:50.980
I'm excited for you to join me.

00:02:51.000 --> 00:02:52.320
And I hope to see you soon.