English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 52 lectures (3h 41m) | 1.50 GB
Learn the NLP Technology Behind AI Tools Like ChatGPT: Understanding, Generating, and Classifying Human Language
Are you passionate about Artificial Intelligence and Natural Language Processing?
Do you want to pursue a career as a data scientist or as an AI engineer?
If that’s the case, then this is the perfect course for you!
In this Intro to Natural Language Processing in Python course you will explore essential topics for working with text data. Whether you want to create custom text classifiers, analyze sentiment, or explore concealed topics, you’ll learn how NLP works and obtain the tools and concepts necessary to tackle these challenges.
Natural language processing is an exciting and rapidly evolving field that fundamentally impacts how we interact with technology. In this course, you’ll learn to unlock the power of natural language processing and will be equipped with the knowledge and skills to start working on your own NLP projects.
The training offers you access to high quality Full HD videos and practical coding exercises. This is a format that facilitates easy comprehension and interactive learning. One of the biggest advantages of all trainings produced by 365 Data Science is their structure. This course makes no exception. The well-organized curriculum ensures you will have an amazing experience.
You won’t need prior natural language processing training to get started—just basic Python skills and familiarity with machine learning.
This introduction to NLP guides you step-by-step through the entire process of completing a project. We’ll cover models and analysis and the fundamentals, such as processing and cleaning text data and how to get data in the correct format for NLP with machine learning.
We’ll utilize algorithms like Latent Dirichlet Allocation, Transformer models, Logistic Regression, Naive Bayes, and Linear SVM, along with such techniques as part-of-speech (POS) tagging and Named Entity Recognition (NER).
You’ll get the opportunity to apply your newly acquired skills through a comprehensive case study, where we’ll guide you through the entire project, covering the following stages:
- Text cleansing
- In-depth content analysis
- Sentiment analysis
- Uncovering hidden themes
- Ultimately crafting a customized text classification model
What you’ll learn
- Natural Language Processing for AI
- Text preprocessing techniques
- Text tagging and entity extraction
- Sentiment analysis
- Uncovering topics in the text
- Text classification
- Vectorizing text for machine learning
Table of Contents
Introduction
1 Introduction to the course
2 Download course materials
3 Introduction to NLP
4 NLP in everyday life
5 Supervised vs Unsupervised NLP
Text Preprocessing
6 The importance of data preparation
7 Setting up the environment
8 Exploring the installed packages
9 Lowercase
10 Removing stop words
11 Regular expressions
12 Tokenization
13 Stemming
14 Lemmatization
15 Ngrams
16 A note on the practical task
17 Practical task
18 Additional notes Text preprocessing with pandas and NLTK
Identifying Parts of Speech and Named Entities
19 Text tagging
20 Parts of speech POS tagging
21 Named entity recognition NER
22 Practical task
Sentiment Analysis
23 What is sentiment analysis
24 Rulebased sentiment analysis
25 Pretrained transformer models
26 Practical task
Vectorizing Text
27 Numerical representation of text
28 Bag of Words model
29 TFIDF
Topic Modelling
30 What is topic modelling
31 When to use topic modelling
32 Latent Dirichlet Allocation
33 LDA in Python
34 Latent Semantic Analysis
35 LSA in Python
36 Determining the number of topics
Building Your Own Text Classifier
37 Building a custom text classifier
38 Logistic regression
39 Naive Bayes
40 Linear Support Vector Machine
Case Study Categorizing Fake News
41 Introducing the project
42 Exploring our data through POS tags
43 Extracting named entities
44 Processing the text
45 Does sentiment differ between news types
46 What topics appear in fake news Part 1
47 What topics appear in fake news Part 2
48 Categorizing fake news with a custom classifier
The Future of NLP
49 What is deep learning
50 Deep learning for NLP
51 NonEnglish NLP
52 Whats next for NLP
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