Intro to NLP for AI

with Lauren Newbould
4.8/5
(215)

Unlock the power of natural language processing and leverage machine learning algorithms in Python to create valuable NLP solutions.

3 hours 48 lessons
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48 High Quality Lessons
0 Practical Tasks
3 Hours of Content
Certificate of Achievement

Course Overview

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.

Topics covered

AINatural Language ProcessingText Classification

What You'll Learn

As we discuss deep learning applications for natural language processing, you’ll see how exciting models like ChatGPT operate behind the scenes. By the end of this Intro to NLP for AI course, you’ll have a solid foundation for working with text data and implementing your own NLP solutions.

Text preprocessing techniques
Text tagging and entity extraction
Sentiment analysis
Uncovering topics in the text
Text classification
Vectorizing text for machine learning

Curriculum

Student feedback

4.8/5

215 ratings
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16.09.2023
Nice course to introduce anyone to the world of NLP. Luckily been working on NLP projects for close to a year now and most of these is pretty intuitive to me. Clear explanations and a great course overall. I loved the topic modelling parts. I remember first having to use Latent Dirichlet Allocation first time at work when I didn't know nothing about it, but luckily had a very good boss who's super good with this stuff.
02.11.2023
First time I am learning Natural language processing, all the topics explained here were organized and easy to understand thanks to the tutorial’s creators. I am excited and looking forward to an advanced version of this course.
13.03.2024
I think that this is a good course for understanding the process of NLP but considering a lot of text data is unstructured, I would have liked more unstructured techniques.
10.03.2024
Excellent course as NLP concepts have explained thoroughly. Also, the coding for NLP in python shown in the course is useful to build on these concepts.
13.02.2024
It is by far not up to the mark compared to other courses, topics discussed are clear but need more clarity or may be a better representation of topics.
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Lauren Newbould

“In this course, we will be delving into the exciting field of NLP and looking at techniques that enable computers to understand, generate, and classify human language.”

Lauren Newbould

Worked at BBC

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Intro to NLP for AI

with Lauren Newbould

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