Technology

Artificial Intelligence for Natural Language Processing

By Theo Riven

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Length3h 32m

About this audiobook

Learn how artificial intelligence helps computers understand, process, and generate human language. Artificial Intelligence for Natural Language Processing is a practical guide for beginners, students, developers, data learners, and professionals who want to master AI-powered text analysis. The book introduces natural language processing in clear terms, explaining how machines analyze words, sentences, meaning, sentiment, patterns, and context. Readers will explore Python for NLP, text cleaning, tokenization, sentiment analysis, chatbots, language models, text classification, entity recognition, and real-world NLP applications. With hands-on guidance and accessible explanations, this book helps readers move from basic concepts to practical AI language projects. Whether you want to build smarter chatbots, analyze customer feedback, process documents, or understand modern language AI, this guide provides the foundation and confidence to begin.

Artificial Intelligence Memory Futuristic Exploration Journey

Audiobook details

GenreTechnology

Length3 hrs 32 mins

Narrated byListen with 1,000+ voices

FormateBook with Audio

Publish dateJun 12, 2026

LanguageEnglish

1Chapter 1: Introduction to AI and Natural Language Processing

2Understanding the Basics: What Is NLP and Why AI Matters

3The Evolution of NLP: From Rule-Based Systems to Machine Learning

4Real-World Applications: Chatbots, Sentiment Analysis, and Beyond

5Setting Up Your Development Environment: Tools, Libraries, and First Steps

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6Hardware and Software Basics

7Key Libraries and Installation

8First Steps: A Hands-On Text Processor

9Summary of Key Takeaways

10Exercises and Challenges

11Chapter 2: Foundations of Python Programming for NLP

12Python Essentials: Data Types, Loops, and Functions Tailored for Text Data

13Key Libraries: Installing and Exploring NLTK, spaCy, and Pandas

14Handling Text Data: Reading, Writing, and Basic Manipulation

15Hands-On Exercise: Building Your First Simple Text Processor

16Summary of Key Takeaways

17Exercises and Challenges

18Chapter 3: Text Preprocessing and Cleaning Techniques

19Tokenization, Stemming, and Lemmatization: Cleaning Noisy Text

20Removing Stop Words, Punctuation, and Handling Special Characters

21Dealing with Multilingual Text and Encoding Issues

22Practical Project: Preprocessing a Dataset of Customer Reviews

23Summary of Key Takeaways

24Exercises and Challenges

25Chapter 4: Feature Engineering for NLP Models

26Bag-of-Words and TF-IDF: Converting Text to Numerical Features

27Word Embeddings: From One-Hot Encoding to Word2Vec

28N-grams and Sequence Features for Contextual Understanding

29Exercise: Engineering Features for a Basic Text Classifier

30Summary of Key Takeaways

31Exercises and Challenges

32Chapter 5: Traditional Machine Learning Algorithms in NLP

33Supervised Learning: Classification with Naive Bayes and SVM

34Unsupervised Learning: Clustering with K-Means and Topic Modeling with LDA

35Evaluation Metrics: Accuracy, Precision, Recall, and F1-Score for NLP Tasks

36Case Study: Building a Spam Detector Using Scikit-Learn

37Summary of Key Takeaways

38Exercises and Challenges

39Chapter 6: Introduction to Deep Learning for Text

40Neural Networks Basics: Perceptrons, Activation Functions, and Backpropagation

41Recurrent Neural Networks (RNNs): Handling Sequential Data

42Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs)

43Hands-On: Training Your First RNN for Sentiment Analysis

44Summary of Key Takeaways

45Exercises and Challenges

46Chapter 7: Advanced Neural Architectures for NLP

47Convolutional Neural Networks (CNNs) for Text: Capturing Local Patterns

48Sequence-to-Sequence Models: Encoder-Decoder Frameworks

49Attention Mechanisms: Why They Revolutionize NLP

50Project: Implementing a Simple Machine Translation Model

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Artificial Intelligence for Natural Language Processing

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