Intro to NLP for AI

with Lauren Newbould
4.7/5
(430)

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

3 hours of content 5106 students

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

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

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

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

$99.00

Lifetime access

Buy now

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

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 handling text data and learn to implement your own NLP solutions
  • Master essential text preprocessing techniques to get your data NLP-ready
  • Gain a deep understanding of how exciting models like ChatGPT operate behind the scenes
  • Learn how to create custom text classifiers, perform sentiment analysis, and uncover hidden topics from text data
  • Be able to vectorize and prepare text data for machine learning modeling
  • Improve your career prospects by mastering NLP, a highly sought-after technical skill that is especially relevant in the current AI-driven tech environment

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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Introduction to the course

1.1 Introduction to the course

3 min

Introduction to NLP

1.3 Introduction to NLP

2 min

NLP in everyday life

1.4 NLP in everyday life

1 min

Supervised vs Unsupervised NLP

1.6 Supervised vs Unsupervised NLP

2 min

The importance of data preparation

2.1 The importance of data preparation

2 min

Interactive Exercises

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

categorize exercise
true-false exercise
single-choice exercise
multiple-choice exercise
flashcards exercise
fill-blank-typing exercise
fill-blank-selection exercise
order exercise

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
See More

Topics

AINatural Language ProcessingText ClassificationPythonWorking with Text DataAIMachine and Deep Learning

Tools & Technologies

python

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.

Exams and certification

Meet Your Instructor

Lauren Newbould

Lauren Newbould

Data Scientist at

3 Courses

726 Reviews

9140 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.

What Our Learners Say

eddriouch aya
eddriouch aya
13.03.2025
good just need more exercices
Mike Baran
Mike Baran
12.03.2025
- I never did get the Running an LSA Model part to work, as I could not come up with compatible gensim and scipy libraries - Lack of markup and comment lines make the code hard to to follow. - Too much wasted empty screen space leaves the notebook text small and difficult to read.
Lai Quang Dung
Lai Quang Dung
08.03.2025

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