Adebukola C.
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Online Course
Intro to NLP for AI
with
Lauren Newbould
Master Natural Language Processing: Leverage Python and Machine Learning to build effective NLP systems
5891 students already have enrolled
Skill level:
Intermediate
Duration:
3 hours
-
Lessons (5 hours)
- Lessons (5 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
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
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
Course OVERVIEW
Description
CPE Credits: 4.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
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
Curriculum
53 lessons
23 exercises
1 exam
- 2. Text Preprocessing41 MinPreprocessing 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.
- 3. Identifying Parts of Speech and Named Entities20 MinLearn how to classify words based on their roles in sentences and how to identify and categorize named entities in your text.
- 4. Sentiment Analysis18 MinUnderstand the fundamentals of sentiment analysis, the methodologies behind it, and the power of pre-trained transformer models in discerning sentiments.
- 5. Vectorizing Text9 MinTextual data in its raw form isn't suitable for machine learning algorithms. Discover techniques to transform text into numerical vectors, enabling computational processes.
- 6. Topic Modelling19 MinDelve 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.
- 7. Builing your own text classifier10 MinFamiliarize yourself with popular algorithms like logistic regression, naive bayes, and support vector machines, and build custom text classifiers tailored to specific needs.
- 8. Case Study: Categorizing Fake News51 MinIn 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 studyIntroducing the projectExploring our data through POS tagsExerciseExtracting named entitiesProcessing the textDoes 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 NLP9 MinLook 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.
- 10. Course exam30 Min
Free preview
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
9 in 10
people walk away career-ready
with practical data and AI skills.
9 in 10
of our graduates landed a new AI & data job
after enrollment
$29,000
average salary increase
after moving to an AI and data science career
ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
Certificates are included with the Self-Study learning plan.
How it WORKS
Student REVIEWS
