Hands On Natural Language Processing (NLP) using Python from Udemy

What's inside

Learning objectives

Understand the various concepts of natural language processing along with their implementation
Build natural language processing based applications
Learn about the different modules available in python for nlp

Create personal spam filter or sentiment predictor
Create personal text summarizer

Understand the various concepts of natural language processing along with their implementation
Build natural language processing based applications
Learn about the different modules available in python for nlp
Create personal spam filter or sentiment predictor
Create personal text summarizer

Syllabus

Introduction to the Course

What is NLP?

Getting the Course Resources

Getting the Course Resources - Text

Getting the required softwares

Installing Anaconda Python

Installing Anaconda Python - Text

A tour of Spyder IDE

How to take this course?

In this section, you will learn all the basic concepts, structures and syntax of Python to keep you going in the rest of the course.

Variables and Operations in Python

Conditional Statements

Introduction to Loops

Loop Control Statements

Python Data Structures - Lists

Python Data Structures - Tuples

Python Data Structures - Dictionaries

Console and File I/O in Python

Introduction to Functions

Introduction to Classes and Objects

List Comprehension

Test Your Skills

In this section you will the basics of regular expressions and how to implement them in Python.

Introduction to Regular Expressions

Finding Patterns in Text Part 1

Finding Patterns in Text Part 2

Substituting Patterns in Text

Shorthand Character Classes

Character Ranges - Text

Preprocessing using Regex

In this section you will the basics of Numpy and Pandas libraries in Python which are necessary for the rest of the course.

Introduction to Numpy

Introduction to Pandas

In this section you learn all the concepts regarding NLP and implement them in Python. You will also be building three different text visualization models.

Installing NLTK in Python

Tokenizing Words and Sentences

How tokenization works? - Text

Introduction to Stemming and Lemmatization

Stemming using NLTK

Lemmatization using NLTK

Stop word removal using NLTK

Parts Of Speech Tagging

POS Tag Meanings

Named Entity Recognition

Text Modelling using Bag of Words Model

Building the BOW Model Part 1

Building the BOW Model Part 2

Building the BOW Model Part 3

Building the BOW Model Part 4

Text Modelling using TF-IDF Model

Building the TF-IDF Model Part 1

Building the TF-IDF Model Part 2

Building the TF-IDF Model Part 3

Building the TF-IDF Model Part 4

Understanding the N-Gram Model

Building Character N-Gram Model

Building Word N-Gram Model

Understanding Latent Semantic Analysis

LSA in Python Part 1

LSA in Python Part 2

Word Synonyms and Antonyms using NLTK

Word Negation Tracking in Python Part 1

Word Negation Tracking in Python Part 2

In this section you will build your own text classifier which will able to predict whether a sentence has a positive or a negative sentiment.

Getting the data for Text Classification

Getting the data for Text Classification - Text

Importing the dataset

Persisting the dataset

Preprocessing the data

Transforming data into BOW Model

Transform BOW model into TF-IDF Model

Creating training and test set

Understanding Logistic Regression

Training our classifier

Testing Model performance

Saving our Model

Importing and using our Model

In this section you will use the classifier built in the previous section to classify real time tweets with positive or negative sentiments.

Setting up Twitter Application

Initializing Tokens

Client Authentication

Fetching real time tweets

Loading TF-IDF Model and Classifier

Preprocessing the tweets

Predicting sentiments of tweets

Plotting the results

In this section you will learn to build your text summarizer which will fetch, pre-process and summarize documents on the go.

Understanding Text Summarization

Fetching article data from the web

Parsing the data using Beautiful Soup

Tokenizing Article into sentences

Building the histogram

Calculating the sentence scores

Getting the summary

In this section you will learn how words can be represented as vectors instead of just numbers and the functionality of the popular Word2Vec/Word Embedding model.

Understanding Word Vectors

Importing the data

Preparing the data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Examines NLP, its applications, and implementation in Python, catering to students with some programming experience

Emphasizes hands-on implementation in Python, making it suitable for learners who want practical experience in NLP

Provides a comprehensive overview of NLP concepts and techniques, offering a strong foundation for learners

Uses industry-standard tools and libraries like NLTK, Pandas, and Numpy, preparing learners for real-world applications

Covers advanced NLP techniques such as Word2Vec, Word Embeddings, and Latent Semantic Analysis

Requires learners to have a basic understanding of Python, potentially limiting accessibility for absolute beginners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Hands On Natural Language Processing (NLP) using Python with these activities:

Practice tokenization and stemming

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Practice tokenization and stemming in advance of taking the course to strengthen your foundational understanding of NLP and reduce time spent on these topics in the course.

Browse courses on Tokenization

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Read the course syllabus and identify sections that cover tokenization and stemming.
Identify online resources and tutorials that provide hands-on exercises for tokenization and stemming
Practice tokenizing text using different techniques, such as word-based and character-based tokenization.
Practice stemming words using different algorithms, such as the Porter stemmer and the Lancaster stemmer.

Complete NLTK Tutorial Exercises

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Work through the exercises in the NLTK tutorial to gain hands-on experience with NLP techniques.

Browse courses on Natural Language Processing

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Set up your Python environment and install NLTK.
Work through the NLTK tutorial exercises.
Experiment with different NLP techniques.

Compile a List of NLP Resources

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Gather and organize a collection of valuable resources, such as tutorials, articles, and tools, to support your NLP learning journey.

Browse courses on Natural Language Processing

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Research and identify reputable NLP resources.
Organize the resources into a central location, such as a document or website.
Share the resource compilation with others.

Six other activities

Expand to see all activities and additional details

Show all nine activities

Practice Writing Regular Expressions

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Write regular expressions to match patterns in text, such as extracting data from unstructured text or validating user input.

Browse courses on Python

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Review basic concepts of regular expressions.
Practice writing basic regular expressions to match patterns.
Test your regular expressions on different text samples.
Apply regular expressions to a real-world task.

Join a Natural Language Processing Study Group

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Connect with other learners and discuss NLP concepts, share resources, and work on projects together.

Browse courses on Natural Language Processing

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Find a study group or start your own.
Meet regularly to discuss NLP topics.
Collaborate on NLP projects.

Read 'Natural Language Processing with Python' by Steven Bird et al.

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Gain a comprehensive understanding of NLP concepts and techniques through this foundational book.

View Natural Language Processing with Python:... on Amazon

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Purchase or borrow the book.
Read the book thoroughly.
Take notes and highlight important concepts.
Complete the exercises provided in the book.

Create a Text Classification Model

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Build a model to classify text into different categories, such as sentiment analysis or spam detection.

Browse courses on Machine Learning

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Collect and prepare a dataset of labeled text data.
Choose and train a machine learning model for text classification.
Evaluate the performance of the model.
Deploy the model for real-world use.

Attend a Natural Language Processing Workshop

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Deepen your understanding of NLP by attending a workshop led by industry experts.

Browse courses on Natural Language Processing

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Find an NLP workshop that aligns with your interests and skill level.
Attend the workshop and actively participate in the sessions.
Apply the knowledge and skills gained from the workshop to your own projects.

Develop a Text Summarization Tool

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Build a tool to automatically summarize text documents, extracting the key points and main ideas.

Browse courses on Natural Language Processing

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Research and understand different text summarization techniques.
Design and implement a text summarization algorithm.
Test and evaluate the performance of the tool.
Package and deploy the tool for real-world use.

Career center

Learners who complete Hands On Natural Language Processing (NLP) using Python will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

This course is directly relevant to the role of a Natural Language Processing Engineer, as it provides a foundation in the field and covers essential concepts such as text classification, text summarization, and word vectors. The hands-on projects would also be valuable experience.

See salaries and explore the career path for Natural Language Processing Engineer

Computational Linguist

A Computational Linguist would find the course's coverage of natural language processing concepts and Python programming to be particularly useful. The course's emphasis on projects, such as building a text classifier and article summarizer, would also be relevant experience.

See salaries and explore the career path for Computational Linguist

Machine Learning Engineer

The course's coverage of natural language processing concepts and Python programming would be useful for a Machine Learning Engineer, as this role often involves working with natural language data. The course's projects, such as building a text classifier and article summarizer, would also be relevant experience.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist would benefit from the course given its emphasis on building natural language processing-based applications, as that skill is essential in the field. The course's hands-on approach and focus on projects are also well aligned with the practical nature of the role.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst would benefit from the course's coverage of natural language processing concepts and Python programming, as these skills are increasingly important in the field. The course's focus on projects, such as building a text classifier and article summarizer, would also be valuable experience.

See salaries and explore the career path for Data Analyst

Software Engineer

The course's coverage of Python programming and natural language processing concepts would be beneficial for a Software Engineer who works on projects involving natural language data. The course's emphasis on building applications would also be relevant experience.

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Product Manager

The course may be useful for a Product Manager who works on products that involve natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.

See salaries and explore the career path for Product Manager

Business Analyst

The course may be useful for a Business Analyst who works on projects involving natural language data, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.

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Technical Writer

The course may be useful for a Technical Writer who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.

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Content Strategist

The course may be useful for a Content Strategist who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.

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Marketing Manager

The course may be useful for a Marketing Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.

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Sales Manager

The course may be useful for a Sales Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.

See salaries and explore the career path for Sales Manager

Operations Manager

The course may be useful for an Operations Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.

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Customer Success Manager

The course may be useful for a Customer Success Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.

See salaries and explore the career path for Customer Success Manager

Project Manager

The course may be useful for a Project Manager who works on projects involving natural language processing, as it would provide a foundation in the field. The course's emphasis on building applications would also be relevant experience.

See salaries and explore the career path for Project Manager