Packt
Applied NLP and Generative AI Specialization
Packt

Applied NLP and Generative AI Specialization

Create AI-Powered NLP Applications in Python. Learn to vectorize, classify, summarize, & deploy NLP apps using machine learning and transformers.

Included with Coursera Plus

Get in-depth knowledge of a subject
Intermediate level

Recommended experience

1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers

What you'll learn

  • Preprocess and vectorize text using tokenization, embeddings, and TF-IDF for NLP applications

  • Build sentiment analysis, spam detection, and summarization tools using machine learning models

  • Apply transformers and Huggingface pipelines for creating generative NLP applications

  • Fine-tune models and deploy AI tools using retrieval-augmented generation and vector databases

Overview

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English
Recently updated!

September 2025

31 practice exercises

Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Packt

Specialization - 3 course series

What you'll learn

  • Grasp key NLP concepts such as tokenization, stopwords, and lemmatization.

  • Master text vectorization with Count Vectorizer and TF-IDF for effective data transformation.

  • Implement neural word embeddings and gain practical experience with text preprocessing for machine learning applications.

Skills you'll gain

Unstructured Data, Text Mining, Machine Learning, Development Environment, Pandas (Python Package), NumPy, Applied Machine Learning, Data Transformation, Scikit Learn (Machine Learning Library), Deep Learning, Feature Engineering, and Data Processing

What you'll learn

  • Build and evaluate spam detection models using Naive Bayes and performance metrics.

  • Implement sentiment analysis with logistic regression in Python.

  • Create extractive summaries using vector methods and TextRank algorithms.

  • Apply LDA, NMF, and LSA techniques for uncovering latent topics in text data.

Skills you'll gain

Python Programming, Dimensionality Reduction, Supervised Learning, Unsupervised Learning, Regression Analysis, Text Mining, Algorithms, Applied Machine Learning, Predictive Modeling, Machine Learning, and Statistical Machine Learning

What you'll learn

  • Utilize Huggingface to implement and fine-tune state-of-the-art NLP models for diverse applications like text classification and summarization.

  • Implement vector databases and advanced neural network techniques for sentiment analysis, word embeddings, and real-world NLP solutions.

  • Apply advanced prompt engineering techniques like chain-of-thought reasoning and RAG to optimize AI performance and tackle complex NLP tasks.

Skills you'll gain

Python Programming, Natural Language Processing, Generative AI, Large Language Modeling, LLM Application, Prompt Engineering, OpenAI, Deep Learning, Databases, Artificial Intelligence, Performance Tuning, and Feature Engineering

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Packt - Course Instructors
Packt
1,000 Courses238,708 learners

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