Natural Language Processing with Real-World Projects

Video description

You will learn how machines can be trained to make sense of the language humans use to interact. You will come across many NLP algorithms that teach computational models about lexical processing and basic syntactic processing. You will learn the mechanism that Google Translator uses, to understand the context of language and convert to a different language. You will build a chatbot using an open-source tool, Rasa, which is a text- and voice-based conversation that understands messages, holds conversations, and connects to messaging channels and APIs. You will also learn to train the model you have created on NLU.

The machine cannot be trained to understand or process data by traditional hand-coded programs that rely heavily on very specific conditions. The moment there is a change in input, the hand-coded program is rendered useless. So, rather than having to code possible conversations, we require a model that enables the system to make sense of context.

Prior knowledge of machine learning and deep learning is beneficial; if not, we have covered all required prerequisites in the course itself.

By the end of the course, you will be able to build NLP models that can summarize blocks of text to extract the most important ideas, sentiment analysis to extract the sentiments from a given block of text and identify the type of entity extracted. All the projects included in this course are real-world projects.

What You Will Learn

  • Introduction to NLP, Regex, and lexical processing
  • Learn basic, intermediate, and advanced syntactic processing
  • Implement syntactic processing in a real-world project
  • Learn the probabilistic approach
  • Learn how to implement parsing in NLP
  • Learn about the CFG/PCFG grammar model

Audience

Students looking to start a career in data science, working professionals with some acquaintance with deep learning, developers looking to create chat-bots, work on Alexa, and Google Home projects will benefit from this course.

About the Author

Geekshub Pvt. Ltd.: Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.

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Table of contents

  1. Chapter 1 : Introduction to NLP (Natural Language Processing) and Regex
    1. Introduction to NLP
    2. Text Data: Part 1
    3. Text Data: Part 2
    4. Text Encoding
    5. Regular Expression: Part 1
    6. Regular Expression: Part 2
    7. Regular Expression: Part 3
    8. Regular Expression: Part 4
    9. Regular Expression: Part 5
    10. Regular Expression: Part 6
    11. Regular Expression: Use Case
  2. Chapter 2 : Introduction to Lexical Processing
    1. Stopwords
    2. Splitting Words
    3. Bag-of-Words
    4. Handling Similar Text Words: Part 1
    5. Handling Similar Text Words: Part 2
    6. Case Study - 1
    7. Tf-IDF
    8. Case Study - 2
    9. Case Study - 3
  3. Chapter 3 : Advanced Lexical Processing
    1. Spelling Mistakes
    2. Soundex Algorithm
    3. Case Study - 1
    4. Dealing with Spelling Mistakes
    5. Case Study - 2
    6. Case Study: Spell Corrector - Part 1
    7. Case Study: Spell Corrector - Part 2
    8. Case Study: Spell Corrector - Part 3
    9. Handling Combined Words Like New Delhi
    10. Handling a Combined Word Like New Delhi 2
  4. Chapter 4 : Basic Syntactic Processing
    1. What Is Syntactic Processing?
    2. Parsing
    3. Grammar for English Sentence: Part 1
    4. Grammar for English Sentence: Part 2
    5. Case Study: Assign Grammar to English Sentence: Part 1
    6. Case Study: Assign Grammar to English Sentence: Part 2
  5. Chapter 5 : Intermediate Syntactic Processing
    1. Stochastic Parsing
    2. Viterbi Algorithm
    3. Hidden Markov Model
    4. Decoding Problem: Part 1
    5. Decoding Problem: Part 2
    6. Learning Hidden Markov Model
    7. Case Study on Syntactic Processing: Part 1
    8. Case Study on Syntactic Processing: Part 2
    9. RNN (Recurrent Neural Network)
  6. Chapter 6 : Advanced Syntactic Processing
    1. Introduction
    2. Issue with Shallow Parsing
    3. CFG Grammar: Part 1
    4. CFG Grammar: Part 2
    5. Top-Dow NumPy n Parsing
    6. Case Study on Advanced Syntactic Processing: Part 1
    7. Bottom Up
    8. Case Study on Advanced Syntactic Processing: Part 2
    9. Practical Issues with the Above Approach
    10. PCFG
  7. Chapter 7 : Probabilistic Approach
    1. Probabilistic CFG Grammar
    2. Case Study
    3. Chomsky Normal Form
    4. Dependency Parsing: Part 1
    5. Dependency Parsing: Part 2
  8. Chapter 8 : Syntactic Processing with Real-World Project
    1. Introduction to Information Extraction Project: Part 1
    2. Case Study: Part 2
    3. Case Study: Part 3
    4. Case Study: Part 4
    5. Case Study: Part 5
    6. Case Study: Part 6
    7. Case Study: Part 7
  9. Chapter 9 : Introduction to Semantic Processing
    1. Introduction
    2. Concepts
    3. Entity
    4. Arity
    5. Reification
    6. Schema
    7. Semantic Associations: Part 1
    8. Semantic Associations: Part 2
    9. Terms and Concepts
    10. Principle of Composition
    11. WordNet
    12. Word Sense Disambiguation
    13. Case Study
  10. Chapter 10 : Advanced Semantic Processing: Part 1
    1. Introduction to Distributional Semantics
    2. Distributional Semantics
    3. Occurrence Matrix: Part 1
    4. Occurrence Matrix: Part 2
    5. Co-Occurrence Matrix
    6. Word Vectors: Part 1
    7. Distance Metric
    8. Word Vectors: Part 2
    9. Understanding Word Embeddings
  11. Chapter 11 : Advanced Semantic Processing: Part 2
    1. LSA - Latent Semantic Analysis
    2. Case Study with LSA
    3. Word2vec: Part 1
    4. Word2vec: Part 2
    5. Case Study: LSA
    6. Case Study: Word2vec: Part 1
    7. Case Study: Word2vec: Part 2
    8. Case Study: Word2vec: Part 3
    9. Case Study: Word2vec: Part 4
    10. Case Study: Classification: Part 1
    11. Case Study: Classification: Part 2
  12. Chapter 12 : Prerequisite - Python Fundamentals
    1. Installation of Python and Anaconda
    2. Python Introduction
    3. Variables in Python
    4. Numeric Operations in Python
    5. Logical Operations
    6. If Else Loop
    7. For While Loop
    8. Functions
    9. Strings: Part 1
    10. Strings: Part 2
    11. List: Part 1
    12. List: Part 2
    13. List: Part 3
    14. List: Part 4
    15. Tuples
    16. Sets
    17. Dictionaries
    18. Comprehension
  13. Chapter 13 : Prerequisite - NumPy
    1. Introduction
    2. NumPy Operations: Part 1
    3. NumPy Operations: Part 2
  14. Chapter 14 : Prerequisite - Pandas
    1. Introduction
    2. Series
    3. DataFrame
    4. Operations: Part 1
    5. Operations: Part 2
    6. Indexes
    7. loc and iloc
    8. Reading CSV
    9. Merging: Part 1
    10. groupby
    11. Merging: Part 2
    12. Pivot Tables
  15. Chapter 15 : Prerequisite - Some Fun with Math
    1. Linear Algebra: Vectors
    2. Linear Algebra: Matrix: Part 1
    3. Linear Algebra: Matrix: Part 2
    4. Linear Algebra: Going from 2D to nD: Part 1
    5. Linear Algebra: Going from 2D to nD: Part 2
  16. Chapter 16 : Prerequisite - Data Visualization
    1. Matplotlib
    2. Seaborn
    3. Case Study
    4. Seaborn on Time Series Data
  17. Chapter 17 : Prerequisite - Simple Linear Regression
    1. Introduction to Machine Learning
    2. Types of Machine Learning
    3. Introduction to Linear Regression (LR)
    4. How LR Works?
    5. Some Fun with Math Behind LR
    6. R Square
    7. LR Case Study: Part 1
    8. LR Case Study: Part 2
    9. LR Case Study: Part 3
    10. Residual Square Error (RSE)
  18. Chapter 18 : Prerequisite - Gradient Descent
    1. Prerequisite for Gradient Descent: Part 1
    2. Prerequisite for Gradient Descent: Part 2
    3. Cost Functions
    4. Defining Cost Functions More Formally
    5. Gradient Descent
    6. Optimization
    7. Closed Form Versus Gradient Descent
    8. Gradient Descent Case Study
  19. Chapter 19 : Prerequisite - Classification: KNN
    1. Introduction to Classification
    2. Defining Classification Mathematically
    3. Introduction to KNN
    4. Accuracy of KNN
    5. Effectiveness of KNN
    6. Distance Metrics
    7. Distance Metrics: Part 2
    8. Finding k
    9. KNN on Regression
    10. Case Study
    11. Classification Case 1
    12. Classification Case 2
    13. Classification Case 3
    14. Classification Case 4
  20. Chapter 20 : Prerequisite - Logistic Regression
    1. Introduction
    2. Sigmoid Function
    3. Log Odds
    4. Case Study
  21. Chapter 21 : Prerequisite - Advanced Machine Learning Algorithms
    1. Introduction
    2. Example: Part 1
    3. Example: Part 2
    4. Optimal Solution
    5. Case Study
    6. Regularization
    7. Ridge and Lasso
    8. Case Study
    9. Model Selection
    10. Adjusted R Square
  22. Chapter 22 : Prerequisite - Deep Learning introduction
    1. Introduction
    2. History of Deep Learning
    3. Perceptron
    4. Multi-Level Perceptron
    5. Neural Network Playground
    6. Representations
    7. Training Neural Network: Part 1
    8. Training Neural Network: Part 2
    9. Training Neural Network: Part 3
    10. Activation Function

Product information

  • Title: Natural Language Processing with Real-World Projects
  • Author(s): Geekshub Pvt. Ltd.
  • Release date: October 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781838980481