Online Course
Intro to NLP for AI

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

with Lauren Newbould

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

4.8

862 reviews on

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7,939 students already enrolled

Skill level:

Intermediate

Duration:

4 hours

Lessons (4 hours)

CPE credits:

4.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

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.

Topics & tools

AINatural Language ProcessingText ClassificationPythonWorking with Text DataMachine and Deep Learning

Your instructor

Lauren Newbould

Senior Data Scientist | Professional Development Expert

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Social Research Methods & Statistics

Media and recognition

Speaker on NLP for social good in developing countries
Advocate for ethical and open data science
Contributor to AI accessibility initiatives

Ask Lauren a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 4.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

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.

Prerequisites

Python (version 3.8 or later), Natural Language Toolkit (NLTK) and pandas libraries, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
Basic understanding of Python programming is required.
No prior experience with natural language processing or machine learning is necessary.

Advanced preparation

Introduction to Python

Curriculum

54 lessons 23 exercises 1 exam

1. Introduction

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.

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 Free

Course Materials and Notebooks

Introduction to NLP Free

NLP in everyday life Free

Exercise

Supervised vs Unsupervised NLP

Exercise
2. Text Preprocessing

57 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.

57 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

Exercise

Setting up the environment

Lowercase

Removing stop words

Regular expressions

Exercise

Tokenization

Stemming

Exercise

Lemmatization

Exercise

N-grams

Exercise

A note on the text preprocessing practical task

Text preprocessing: Practical task
3. Identifying Parts of Speech and Named Entities

33 min

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

33 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

Exercise

Parts of speech (POS) tagging

Named entity recognition (NER)

Exercise

A note on the POS and NER practical task

POS and NER: Practical task
4. Sentiment Analysis

26 min

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

26 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?

Rule-based sentiment analysis

Exercise

Pre-trained transformer models

A note on the sentiment analysis practical task

Sentiment analysis: Practical task
5. Vectorizing Text

10 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.

10 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

Exercise

Bag of Words Model

Exercise

TF-IDF
6. Topic Modelling

25 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.

25 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?

Exercise

When to use topic modelling?

Exercise

Latent Dirichlet Allocation

Exercise

A note on the following lesson

LDA in python

Latent Semantic Analysis

Exercise

LSA in python

How many topics?

Exercise
7. Builing your own text classifier

17 min

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

17 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

Logistic regression

Exercise

Naive Bayes

Exercise

Linear Support Vector Machine
8. Case Study: Categorizing Fake News

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.

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

Introducing the project

Exploring our data through POS tags

Exercise

Extracting named entities

Processing the text

Does sentiment differ between news types?

What topics appear in fake news? (Part 1)

What topics appear in fake news? (Part 2)

Categorizing fake news with a custom classifier
9. The Future of NLP

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.

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?

Exercise

Deep learning for NLP

Exercise

Non-English NLP

What's next for NLP?
10. Course exam

30 min

30 min

Course exam

Free lessons

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

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This course is highly recommended, and the Tutor did a great job as all topics were excellently delivered. I totally enjoyed it notwithstanding being a newbie.

Adebukola C.

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9 in 10

people walk away career-ready

with practical data and AI skills.

So far, I am in love with this course. Being taught in a very clear, organized manner. And the medal is to the recap questions because they really come through in solidifying one's understanding.

Isaac K.

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9 in 10

of our graduates landed a new AI & data job

after enrollment

Coming from a non-data background, I was overwhelmed by the scattered content online. 365 Data Science stood out with its structured courses, knowledgeable instructors, and in-depth coverage. After enrolling in the one-year program and completing the Data Science Bootcamp, I gained the skills and confidence to ace tough coding tests and interviews—landing an analyst role at Meesho. The platform made learning clear, practical, and effective, and I’m deeply grateful for the journey.