Description

Want to become an expert NLP engineer and a data scientist? Then this is the right course for you. In this course, we will be covering complex theory, algorithms, and coding libraries in a very simple way that can be easily grasped by any beginner as well.

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. All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Natural-Language-Processing-with-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.

Approach

Students engage with real-world projects after each subject, which greatly enhances their learning. Throughout this course, we will also complete a few real-world projects, for which complete solutions have been provided so that you may quickly put what has been learned into practice. Step-by-step instructions are provided with detailed explanations of case studies.

Key Features

Master Natural Language Processing using Python * Master machine learning in Python * Build a foundation for Python, machine learning, and deep learning in the prerequisite section

Github Repo

https://github.com/packtpublishing/natural-language-processing-with-real-world-projects

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.

Course Outline

1. Introduction to NLP (Natural Language Processing) and Regex

In this section, we will have a quick introduction to NLP and Regex.

1. Introduction to NLP

In this video, we will have a quick introduction to NLP.

2. Text Data: Part 1

In this first part, we will work on text data.

3. Text Data: Part 2

In this second part, we will continue with text data.

4. Text Encoding

In this video, we will cover text encoding.

5. Regular Expression: Part 1

In this first part, we will work on regular expressions.

6. Regular Expression: Part 2

In this second part, we will continue with regular expressions.

7. Regular Expression: Part 3

In this third part, we will continue with regular expressions.

8. Regular Expression: Part 4

In this 4th part, we will continue with regular expressions.

9. Regular Expression: Part 5

In this 5th part, we will continue with regular expressions.

10. Regular Expression: Part 6

In this 6th part, we will continue with regular expressions.

11. Regular Expression: Use Case

In this video, we will work on a use case for regular expressions.

2. Introduction to Lexical Processing

In this section, we will have a quick introduction to lexical processing.

1. Stopwords

In this video, we will cover Stopwords.

2. Splitting Words

In this video, we will cover splitting words.

3. Bag-of-Words

In this video, we will cover bag-of-words.

4. Handling Similar Text Words: Part 1

In this first part, you will learn how to handle similar text words.

5. Handling Similar Text Words: Part 2

In this second part, we will continue with handling similar text words.

6. Case Study - 1

In this case study video, we will use the bag of word model example.

7. Tf-IDF

In this video, we will cover Tf-IDF.

8. Case Study - 2

In this case study video, we will use the Tf-IDF model.

9. Case Study - 3

In this case study video, we will work with spam ham detection.

3. Advanced Lexical Processing

In this section, we will cover advanced lexical processing.

1. Spelling Mistakes

In this video, we will build a model that will help us with spelling mistakes.

2. Soundex Algorithm

In this video, we will cover the Soundex algorithm.

3. Case Study - 1

In this case study video, you will learn how to create a soundex function.

4. Dealing with Spelling Mistakes

In this video, we will build a model that will help us to deal with spelling mistakes.

5. Case Study - 2

In this case study video, we will work on Levenshtein Distance.

6. Case Study: Spell Corrector - Part 1

In this case study video, we will be working on building a spell-corrector.

7. Case Study: Spell Corrector - Part 2

In this case study video, we will continue with building our spell-corrector.

8. Case Study: Spell Corrector - Part 3

In this case study video, we will continue with building our spell-corrector.

9. Handling Combined Words Like New Delhi

In this video, we will build a model that will help us in handling combined words like New Delhi.

10. Handling a Combined Word Like New Delhi 2

In this second part, we will continue with building a model for combined words.

4. Basic Syntactic Processing

In this section, we will cover basic syntactic processing.

1. What Is Syntactic Processing?

In this video, we will understand syntactic processing.

2. Parsing

In this video, we will cover parsing.

3. Grammar for English Sentence: Part 1

In this first part, we will work on grammar for an English sentence.

4. Grammar for English Sentence: Part 2

In this second part, we will continue with grammar for an English sentence.

5. Case Study: Assign Grammar to English Sentence: Part 1

In this case study video, we will look at the two most basic tagging techniques-lexicon-based (or unigram) and rule-based.

6. Case Study: Assign Grammar to English Sentence: Part 2

In this case study video, we will continue with lexicon- and rule-based taggers.

5. Intermediate Syntactic Processing

In this section, we will cover intermediate syntactic processing.

1. Stochastic Parsing

In this video, you will learn about stochastic parsing.

2. Viterbi Algorithm

In this video, you will learn about the Viterbi algorithm.

3. Hidden Markov Model

In this video, you will learn about the Hidden Markov model.

4. Decoding Problem: Part 1

In this first part, we will work on a decoding problem.

5. Decoding Problem: Part 2

In this second part, we will continue with a decoding problem.

6. Learning Hidden Markov Model

In this video, we will cover the Hidden Markov model.

7. Case Study on Syntactic Processing: Part 1

In this case study video, we will work on POS Tagging, HMMs, and Viterbi.

8. Case Study on Syntactic Processing: Part 2

In this case study video, we will continue with POS Tagging, HMMs, and Viterbi.

9. RNN (Recurrent Neural Network)

In this video, you will learn about RNN.

6. Advanced Syntactic Processing

In this section, we will cover advanced syntactic processing.

1. Introduction

In this video, we will have a quick introduction.

2. Issue with Shallow Parsing

In this video, we will cover an issue with shallow parsing.

3. CFG Grammar: Part 1

In this first part, we will work on CFG Grammar.

4. CFG Grammar: Part 2

In this 2nd part, we will continue with CFG Grammar.

5. Top-Down Parsing

In this video, we will cover top-down parsing.

6. Case Study on Advanced Syntactic Processing: Part 1

In this case study video, we will perform the top-down parsing approach.

7. Bottom Up

In this video, we will cover the bottom up.

8. Case Study on Advanced Syntactic Processing: Part 2

In this case study video, we will continue with the top-down parsing approach.

9. Practical Issues with the Above Approach

In this video, we will cover practical issues with the above approach.

10. PCFG

In this video, we will cover PCFG.

7. Probabilistic Approach

In this section, we will cover the probabilistic approach.

1. Probabilistic CFG Grammar

In this video, we will cover Probabilistic CFG Grammar.

2. Case Study

In this video, we will work on a case study.

3. Chomsky Normal Form

In this video, we will cover Chomsky Normal Form.

4. Dependency Parsing: Part 1

In this first part, we will work on dependency parsing.

5. Dependency Parsing: Part 2

In this second part, we will continue with dependency parsing.

8. Syntactic Processing with Real-World Project

In this section, we will cover syntactic processing with a real-world project.

1. Introduction to Information Extraction Project: Part 1

In this video, we will have a quick introduction to the information extraction project.

2. Case Study: Part 2

In this case study video, we will perform a case study using ATIS flight reservations - information extraction.

3. Case Study: Part 3

In this case study video, we will continue with the airlines case study.

4. Case Study: Part 4

In this case study video, we will continue with the airlines case study.

5. Case Study: Part 5

In this case study video, we will continue with the airlines case study.

6. Case Study: Part 6

In this case study video, we will continue with the airlines case study.

7. Case Study: Part 7

In this case study video, we will continue with the airlines case study.

9. Introduction to Semantic Processing

In this section, we will cover the introduction to semantic processing.

1. Introduction

In this video, we will have a quick introduction.

2. Concepts

In this video, you will learn about concepts.

3. Entity

In this video, you will learn about Entity.

4. Arity

In this video, you will learn about Arity.

5. Reification

In this video, you will learn about Reification.

6. Schema

In this video, you will learn about Schema.

7. Semantic Associations: Part 1

In this first part, we will work on semantic associations.

8. Semantic Associations: Part 2

In this second part, we will continue with semantic associations.

9. Terms and Concepts

In this video, you will learn about terms and concepts.

10. Principle of Composition

In this video, you will learn about the principle of composition.

11. WordNet

In this video, you will learn about WordNet.

12. Word Sense Disambiguation

In this video, you will learn about word sense disambiguation.

13. Case Study

In this case study video, we will use the Lesk algorithm.

10. Advanced Semantic Processing: Part 1

In this section, we will cover advanced semantic processing: part 1.

1. Introduction to Distributional Semantics

In this video, we will have a quick introduction to distributional semantics.

2. Distributional Semantics

In this video, we will cover distributional semantics.

3. Occurrence Matrix: Part 1

In this first part, we will work on the occurrence matrix.

4. Occurrence Matrix: Part 2

In this second part, we will continue with the occurrence matrix.

5. Co-Occurrence Matrix

In this video, we will cover the co-occurrence matrix.

6. Word Vectors: Part 1

In this first part, we will work on word vectors.

7. Distance Metric

In this video, we will cover the distance metric.

8. Word Vectors: Part 2

In this second part, we will continue with word vectors.

9. Understanding Word Embeddings

In this video, we will understand how word embeddings work.

11. Advanced Semantic Processing: Part 2

In this section, we will cover advanced semantic processing: part 2.

1. LSA - Latent Semantic Analysis

In this video, you will learn about LSA - Latent Semantic Analysis.

2. Case Study with LSA

In this video, we will work on a case study.

3. Word2vec: Part 1

In this first part, we will work on Word2vec.

4. Word2vec: Part 2

In this second part, we will continue with Word2vec.

5. Case Study: LSA

In this video, we will work on a case study.

6. Case Study: Word2vec: Part 1

In this video, we will work on a case study.

7. Case Study: Word2vec: Part 2

In this video, we will work on a case study.

8. Case Study: Word2vec: Part 3

In this video, we will work on a case study.

9. Case Study: Word2vec: Part 4

In this video, we will work on a case study.

10. Case Study: Classification: Part 1

In this video, we will work on a case study.

11. Case Study: Classification: Part 2

In this video, we will work on a case study.

12. Prerequisite - Python Fundamentals

In this pre-req section, we will cover Python fundamentals.

1. Installation of Python and Anaconda

In this video, we will cover the steps for the installation of Python and Anaconda.

2. Python Introduction

In this video, we will have a quick introduction to Python.

3. Variables in Python

In this video, you will learn about variables in Python.

4. Numeric Operations in Python

In this video, you will learn about numeric operations in Python.

5. Logical Operations

In this video, you will learn about logical operations.

6. If Else Loop

In this video, you will learn about the if else loop.

7. For While Loop

In this video, you will learn about the for while loop.

8. Functions

In this video, you will learn about functions.

9. Strings: Part 1

In this first part, we will work on strings.

10. Strings: Part 2

In this second part, we will continue with strings.

11. List: Part 1

In this first part, we will work on list.

12. List: Part 2

In this second part, we will continue with list.

13. List: Part 3

In this third part, we will continue with list.

14. List: Part 4

In this 4th part, we will continue with list.

15. Tuples

In this video, you will learn about tuples.

16. Sets

In this video, you will learn about sets.

17. Dictionaries

In this video, you will learn about dictionaries.

18. Comprehension

In this video, you will learn about comprehension.

13. Prerequisite - NumPy

In this prerequisite section, we will cover NumPy.

1. Introduction

In this video, we will have a quick introduction.

2. NumPy Operations: Part 1

In this first part, we will work on NumPy operations.

3. NumPy Operations: Part 2

In this second part, we will continue with NumPy operations.

14. Prerequisite - Pandas

In this prerequisite section, we will cover Pandas.

1. Introduction

In this video, we will have a quick introduction.

2. Series

In this video, you will learn about Series.

3. DataFrame

In this video, you will learn about DataFrame.

4. Operations: Part 1

In this first part, we will work on operations.

5. Operations: Part 2

In this second part, we will continue with operations.

6. Indexes

In this video, you will learn about indexes.

7. loc and iloc

In this video, you will learn about loc and iloc.

8. Reading CSV

In this video, you will learn how to read CSV.

9. Merging: Part 1

In this first part, we will work on merging.

10. groupby

In this video, you will learn about groupby.

11. Merging: Part 2

In this second part, we will continue with merging.

12. Pivot Tables

In this video, you will learn about pivot tables.

15. Prerequisite - Some Fun with Math

In this pre-req section, we will have some fun with math.

1. Linear Algebra: Vectors

In this video, you will learn about vectors.

2. Linear Algebra: Matrix: Part 1

In this first part, we will work on linear algebra and cover matrix.

3. Linear Algebra: Matrix: Part 2

In this second part, we will continue with matrix.

4. Linear Algebra: Going from 2D to nD: Part 1

In this first part, we will work on linear algebra and learn how to go from 2D to nD.

5. Linear Algebra: Going from 2D to nD: Part 2

In this second part, we will continue learning how to go from 2D to nD.

16. Prerequisite - Data Visualization

In this pre-req section, we will cover data visualization.

1. Matplotlib

In this video, you will learn about Matplotlib.

2. Seaborn

In this video, you will learn about Seaborn.

3. Case Study

In this video, we will work on a case study.

4. Seaborn on Time Series Data

In this video, you will learn about Seaborn on time series data.

17. Prerequisite - Simple Linear Regression

In this pre-req section, we will cover simple linear regression.

1. Introduction to Machine Learning

In this video, we will have a quick introduction to machine learning.

2. Types of Machine Learning

In this video, you will learn about types of machine learning.

3. Introduction to Linear Regression (LR)

In this video, we will have a quick introduction to linear regression (LR).

4. How LR Works?

In this video, we will understand how LR works.

5. Some Fun with Math Behind LR

In this video, we will have some fun with math behind LR.

6. R Square

In this video, you will learn about R Square.

7. LR Case Study: Part 1

In this video, we will work on a case study.

8. LR Case Study: Part 2

In this video, we will work on a case study.

9. LR Case Study: Part 3

In this video, we will work on a case study.

10. Residual Square Error (RSE)

In this video, you will learn about Residual Square Error (RSE).

18. Prerequisite - Gradient Descent

In this pre-req section, we will cover gradient descent.

1. Prerequisite for Gradient Descent: Part 1

In this first part, we will work on the prerequisite for gradient descent.

2. Prerequisite for Gradient Descent: Part 2

In this second part, we will continue with the prerequisite for gradient descent.

3. Cost Functions

In this video, you will learn about cost functions.

4. Defining Cost Functions More Formally

In this video, you will learn how to define cost functions more formally.

5. Gradient Descent

In this video, you will learn about gradient descent.

6. Optimization

In this video, you will learn about optimization.

7. Closed Form Versus Gradient Descent

In this video, you will learn about closed form versus gradient descent.

8. Gradient Descent Case Study

In this video, we will work on a case study.

19. Prerequisite - Classification: KNN

In this pre-req section, we will cover classification with KNN.

1. Introduction to Classification

In this video, we will have a quick introduction to classification.

2. Defining Classification Mathematically

In this video, you will learn how to define classification mathematically.

3. Introduction to KNN

In this video, we will have a quick introduction to KNN.

4. Accuracy of KNN

In this video, you will learn about the accuracy of KNN.

5. Effectiveness of KNN

In this video, you will learn about the effectiveness of KNN.

6. Distance Metrics

In this video, you will learn about distance metrics.

7. Distance Metrics: Part 2

In this second part, we will continue with distance metrics.

8. Finding k

In this video, you will learn how to find k.

9. KNN on Regression

In this video, you will learn about KNN on regression.

10. Case Study

In this video, we will work on a case study.

11. Classification Case 1

In this video, we will work on the classification case 1.

12. Classification Case 2

In this video, we will work on the classification case 2.

13. Classification Case 3

In this video, we will work on the classification case 3.

14. Classification Case 4

In this video, we will work on the classification case 4.

20. Prerequisite - Logistic Regression

In this prerequisite section, we will cover logistic regression.

1. Introduction

In this video, we will have a quick introduction.

2. Sigmoid Function

In this video, you will learn about the Sigmoid function.

3. Log Odds

In this video, you will learn about Log Odds.

4. Case Study

In this video, we will work on a case study.

21. Prerequisite - Advanced Machine Learning Algorithms

In this pre-req section, we will cover advanced machine learning algorithms.

1. Introduction

In this video, we will have a quick introduction.

2. Example: Part 1

In this first part, we will work on an example.

3. Example: Part 2

In this second part, we will continue with the example.

4. Optimal Solution

In this video, we will cover the optimal solution.

5. Case Study

In this video, we will work on a case study.

6. Regularization

In this video, you will learn about regularization.

7. Ridge and Lasso

In this video, you will learn about Ridge and Lasso.

8. Case Study

In this video, we will work on a case study.

9. Model Selection

In this video, you will learn about model selection.

10. Adjusted R Square

In this video, you will learn about Adjusted R Square.

22. Prerequisite - Deep Learning introduction

In this pre-req section, we will have a quick introduction to deep learning.

1. Introduction

In this video, we will have a quick introduction.

2. History of Deep Learning

In this video, we will cover the history of deep learning.

3. Perceptron

In this video, you will learn about Perceptron.

4. Multi-Level Perceptron

In this video, you will learn about multi-level perceptron.

5. Neural Network Playground

In this video, you will learn about neural network playground.

6. Representations

In this video, you will learn about Representations.

7. Training Neural Network: Part 1

In this first part, you will learn how to train the neural network.

8. Training Neural Network: Part 2

In this second part, we will continue with training the neural network.

9. Training Neural Network: Part 3

In this third part, we will continue working on training the neural network.

10. Activation Function

In this video, you will learn about the activation function.

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Natural Language Processing with Real-World Projects

By Packt

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Highlights

Description

What You Will Learn

Audience

Approach

Key Features

Github Repo

About the Author

Geekshub Pvt. Ltd.

Course Outline

1. Introduction to NLP (Natural Language Processing) and Regex

2. Introduction to Lexical Processing

3. Advanced Lexical Processing

4. Basic Syntactic Processing

5. Intermediate Syntactic Processing

6. Advanced Syntactic Processing

7. Probabilistic Approach

8. Syntactic Processing with Real-World Project

9. Introduction to Semantic Processing

10. Advanced Semantic Processing: Part 1

11. Advanced Semantic Processing: Part 2

12. Prerequisite - Python Fundamentals

13. Prerequisite - NumPy

14. Prerequisite - Pandas

15. Prerequisite - Some Fun with Math

16. Prerequisite - Data Visualization

17. Prerequisite - Simple Linear Regression

18. Prerequisite - Gradient Descent

19. Prerequisite - Classification: KNN

20. Prerequisite - Logistic Regression

21. Prerequisite - Advanced Machine Learning Algorithms

22. Prerequisite - Deep Learning introduction

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