Intro to NLP for AI

Name: Intro to NLP for AI Course
Price: 36 USD

with Lauren Newbould

4.7/5

(481)

Master Natural Language Processing: Leverage Python and Machine Learning to build effective NLP systems

3 hours of content 5885 students

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What you get:

3 hours of content
23 Interactive exercises
10 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Intro to NLP for AI

A course by Lauren Newbould

Start for Free

What you get:

3 hours of content
23 Interactive exercises
10 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Start for Free

What you get:

3 hours of content
23 Interactive exercises
10 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What You Learn

Acquire a solid foundation in NLP and text data handling.
Master text preprocessing to prepare data for NLP tasks.
Understand how models like ChatGPT work behind the scenes.
Create text classifiers and perform sentiment and topic analysis.
Vectorize and prepare text data for machine learning modeling.

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

Natural language processing is an exciting and rapidly evolving data science field that fundamentally impacts how we interact with technology. In this Intro to NLP for AI course, you’ll learn to unlock the power of natural language processing and be equipped with the knowledge and skills to start working on your own NLP projects. You’ll explore essential topics for working with text data via video lessons and practical coding exercises. 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. 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 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.

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1.1 Introduction to the course

3 min

1.3 Introduction to NLP

2 min

1.4 NLP in everyday life

1 min

1.6 Supervised vs Unsupervised NLP

2 min

2.1 The importance of data preparation

2 min

Curriculum

1. Introduction

5 Lessons 9 Min

An introduction to the world of Natural Language Processing (NLP). Get acquainted with the basic concepts of NLP and understand its significance in today's world.

Introduction to the course
3 min
Course Materials and Notebooks Read now
1 min
Introduction to NLP
2 min
NLP in everyday life
1 min
Supervised vs Unsupervised NLP
2 min
2. Text Preprocessing

10 Lessons 41 Min

Preprocessing is a fundamental part of any NLP task. Learn about the various techniques employed to clean and prepare textual data, ranging from basic tasks like lowercasing to more complex ones like tokenization and lemmatization.

The importance of data preparation
2 min
Lowercase
2 min
Removing stop words
4 min
Regular expressions
10 min
Tokenization
3 min
Stemming
3 min
Lemmatization
2 min
N-grams
4 min
A note on the text preprocessing practical task Read now
1 min
Text preprocessing: Practical task
10 min
3. Identifying Parts of Speech and Named Entities

5 Lessons 20 Min

Learn how to classify words based on their roles in sentences and how to identify and categorize named entities in your text.

Text Tagging
1 min
Parts of speech (POS) tagging
4 min
Named entity recognition (NER)
4 min
A note on the POS and NER practical task Read now
1 min
POS and NER: Practical task
10 min
4. Sentiment Analysis

5 Lessons 18 Min

Understand the fundamentals of sentiment analysis, the methodologies behind it, and the power of pre-trained transformer models in discerning sentiments.

What is sentiment analysis?
2 min
Rule-based sentiment analysis
5 min
Pre-trained transformer models
4 min
A note on the sentiment analysis practical task Read now
1 min
Sentiment analysis: Practical task
6 min
5. Vectorizing Text

3 Lessons 9 Min

Textual data in its raw form isn't suitable for machine learning algorithms. Discover techniques to transform text into numerical vectors, enabling computational processes.

Numerical representation of text
2 min
Bag of Words Model
3 min
TF-IDF
4 min
6. Topic Modelling

8 Lessons 19 Min

Delve into the art of extracting underlying topics from vast text-based content. Explore various methods like Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). Learn how to determine the optimal number of topics.

What is topic modelling?
3 min
When to use topic modelling?
2 min
Latent Dirichlet Allocation
2 min
A note on the following lesson Read now
1 min
LDA in python
4 min
Latent Semantic Analysis
2 min
LSA in python
1 min
How many topics?
4 min
7. Builing your own text classifier

4 Lessons 10 Min

Familiarize yourself with popular algorithms like logistic regression, naive bayes, and support vector machines, and build custom text classifiers tailored to specific needs.

Building a custom text classifier
1 min
Logistic regression
5 min
Naive Bayes
2 min
Linear Support Vector Machine
2 min
8. Case Study: Categorizing Fake News

9 Lessons 51 Min

In this section of the course, you will have the opportunity to work on a real-world application of NLP. Through this case study, understand the nuances of fake news, process and analyze the data, and build a classifier to segregate genuine news from the fabricated ones.

A note on the case study Read now
1 min
Introducing the project
4 min
Exploring our data through POS tags
9 min
Extracting named entities
5 min
Processing the text
9 min
Does sentiment differ between news types?
5 min
What topics appear in fake news? (Part 1)
6 min
What topics appear in fake news? (Part 2)
6 min
Categorizing fake news with a custom classifier
6 min
9. The Future of NLP

4 Lessons 9 Min

Look ahead into the future of NLP. Understand the role of deep learning in advancing NLP, explore the challenges and opportunities in non-English NLP, and ponder on what the future holds for this dynamic field.

What is deep learning?
3 min
Deep learning for NLP
2 min
Non-English NLP
2 min
What's next for NLP?
2 min

Topics

AINatural Language ProcessingText ClassificationPythonWorking with Text DataMachine and Deep Learning

Tools & Technologies

Course Requirements

Highly recommended to take the Intro to Python course first
You will need to install the Anaconda package, which includes Jupyter Notebook

Who Should Take This Course?

Level of difficulty: Intermediate

Aspiring data analysts, data scientists, data engineers, machine learning engineers, and AI Engineers

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Lauren Newbould

Data Scientist at

3 Courses

849 Reviews

11002 Students

Guided by a comprehensive social science and statistics background, Lauren's data science career has taken her through several pivotal roles—from creating custom NLP solutions for non-profits in Nepal to providing insights for BBC Sport and the 2020 Olympics. Lauren has spoken at several conferences on how NLP can benefit those in developing countries and advocates for ethical and open data science. She aims to empower individuals and organizations to make confident, data-driven decisions and to ensure AI is fair and accessible for all.