Intro to Natural Language Processing in Python for AI

Intro to Natural Language Processing in Python for AI

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 46 lectures (2h 52m) | 1.27 GB

Learn the 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

1 Introduction to the course
2 Introduction to NLP
3 NLP in everyday life
4 Supervised vs Unsupervised NLP

Text Preprocessing
5 The importance of data preparation
6 Lowercase
7 Removing stop words
8 Regular expressions
9 Tokenization
10 Stemming
11 Lemmatization
12 Ngrams
13 Practical task

Identifying Parts of Speech and Named Entities
14 Text tagging
15 Parts of speech POS tagging
16 Named entity recognition NER
17 Practical task

Sentiment Analysis
18 What is sentiment analysis
19 Rulebased sentiment analysis
20 Pretrained transformer models
21 Practical task

Vectorizing Text
22 Numerical representation of text
23 Bag of Words model

Topic Modelling
25 What is topic modelling
26 When to use topic modelling
27 Latent Dirichlet Allocation
28 LDA in Python
29 Latent Semantic Analysis
30 LSA in Python

Builing Your Own Text Classifier
31 Building a custom text classifier
32 Logistic regression
33 Naive Bayes
34 Linear Support Vector Machine

Case Study Categorizing Fake News
35 Introducing the project
36 Exploring our data through POS tags
37 Extracting named entities
38 Processing the text
39 Does sentiment differ between news types
40 What topics appear in fake news Part 1
41 What topics appear in fake news Part 2
42 Categorizing fake news with a custom classifier

The Future of NLP
43 What is deep learning
44 Deep learning for NLP
45 NonEnglish NLP
46 Whats next for NLP