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2024 Natural Language Processing in Python for Beginners

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2024 Natural Language Processing in Python for Beginners, available at $79.99, has an average rating of 4.53, with 382 lectures, based on 932 reviews, and has 19926 subscribers.

You will learn about Learn complete text processing with Python Learn how to extract text from PDF files Use Regular Expressions for search in text Use SpaCy and NLTK to extract complete text features from raw text Use Latent Dirichlet Allocation for Topic Modelling Use Scikit-Learn and Deep Learning for Text Classification Learn Multi-Class and Multi-Label Text Classification Use Spacy and NLTK for Sentiment Analysis Understand and Build word2vec and GloVe based ML models Use Gensim to obtain pretrained word vectors and compute similarities and analogies Learn Text Summarization and Text Generation using LSTM and GRU Understand the basic concepts and techniques of natural language processing and their applications. Learn how to use Python and its popular libraries such as NLTK and spaCy to perform common NLP tasks. Be able to tokenize and stem text data using Python. Understand and apply common NLP techniques such as sentiment analysis, text classification, and named entity recognition. Learn how to apply NLP techniques to real-world problems and projects. Understand the concept of topic modeling and implement it using Python. Learn the basics of text summarization and its implementation using Python. Understand the concept of text generation and implement it using Python Understand the concept of text-to-speech and speech-to-text conversion and implement them using Python. Learn how to use deep learning techniques for NLP such as RNN, LSTM, and word embedding. This course is ideal for individuals who are Beginners in Natural Language Processing or Data Scientist curious to learn NLP or Individuals with a basic understanding of Python programming who want to expand their skills to include natural language processing or Data scientists, data analysts, and researchers who want to add NLP to their toolkit or Developers who want to build applications that involve natural language processing, such as chatbots or text-based recommender systems or Students and professionals in fields such as linguistics, computer science, and artificial intelligence who want to gain a deeper understanding of NLP It is particularly useful for Beginners in Natural Language Processing or Data Scientist curious to learn NLP or Individuals with a basic understanding of Python programming who want to expand their skills to include natural language processing or Data scientists, data analysts, and researchers who want to add NLP to their toolkit or Developers who want to build applications that involve natural language processing, such as chatbots or text-based recommender systems or Students and professionals in fields such as linguistics, computer science, and artificial intelligence who want to gain a deeper understanding of NLP.

Enroll now: 2024 Natural Language Processing in Python for Beginners

Summary

Title: 2024 Natural Language Processing in Python for Beginners

Price: $79.99

Average Rating: 4.53

Number of Lectures: 382

Number of Published Lectures: 382

Number of Curriculum Items: 382

Number of Published Curriculum Objects: 382

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn complete text processing with Python
  • Learn how to extract text from PDF files
  • Use Regular Expressions for search in text
  • Use SpaCy and NLTK to extract complete text features from raw text
  • Use Latent Dirichlet Allocation for Topic Modelling
  • Use Scikit-Learn and Deep Learning for Text Classification
  • Learn Multi-Class and Multi-Label Text Classification
  • Use Spacy and NLTK for Sentiment Analysis
  • Understand and Build word2vec and GloVe based ML models
  • Use Gensim to obtain pretrained word vectors and compute similarities and analogies
  • Learn Text Summarization and Text Generation using LSTM and GRU
  • Understand the basic concepts and techniques of natural language processing and their applications.
  • Learn how to use Python and its popular libraries such as NLTK and spaCy to perform common NLP tasks.
  • Be able to tokenize and stem text data using Python.
  • Understand and apply common NLP techniques such as sentiment analysis, text classification, and named entity recognition.
  • Learn how to apply NLP techniques to real-world problems and projects.
  • Understand the concept of topic modeling and implement it using Python.
  • Learn the basics of text summarization and its implementation using Python.
  • Understand the concept of text generation and implement it using Python
  • Understand the concept of text-to-speech and speech-to-text conversion and implement them using Python.
  • Learn how to use deep learning techniques for NLP such as RNN, LSTM, and word embedding.

Who Should Attend

  • Beginners in Natural Language Processing
  • Data Scientist curious to learn NLP
  • Individuals with a basic understanding of Python programming who want to expand their skills to include natural language processing
  • Data scientists, data analysts, and researchers who want to add NLP to their toolkit
  • Developers who want to build applications that involve natural language processing, such as chatbots or text-based recommender systems
  • Students and professionals in fields such as linguistics, computer science, and artificial intelligence who want to gain a deeper understanding of NLP

Target Audiences

  • Beginners in Natural Language Processing
  • Data Scientist curious to learn NLP
  • Individuals with a basic understanding of Python programming who want to expand their skills to include natural language processing
  • Data scientists, data analysts, and researchers who want to add NLP to their toolkit
  • Developers who want to build applications that involve natural language processing, such as chatbots or text-based recommender systems
  • Students and professionals in fields such as linguistics, computer science, and artificial intelligence who want to gain a deeper understanding of NLP

Welcome to KGP Talkie’s Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python.

We will learn Spacy in detail and we will also explore the uses of NLP in real life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python. At the end part of this course, you will learn how to generate poetry by using LSTM. Multi-Label and Multi-class classification is explained. At least 12 NLP Projects are covered in this course. You will learn various ways of solving edge-cutting NLP problems.

You should have an introductory knowledge of Python and Machine Learning before enrolling in this course.

In this course, we will start from level 0 to the advanced level.

We will start with basics like what is machine learning and how it works. Thereafter I will take you to Python, Numpy, and Pandas crash course. If you have prior experience you can skip these sections. The real game of NLP will start with Spacy Introduction where I will take you through various steps of NLP preprocessing. We will be using Spacy and NLTK mostly for the text data preprocessing.

In the next section, we will learn about working with files to store and load text data. This section is the foundation of another section on Complete Text Preprocessing. I will show you many ways of text preprocessing using Spacy and Regular Expressions. Finally, I will show you how you can create your own python package on preprocessing. It will help us to improve our code-writing skills. We will be able to reuse our code systemwide without writing codes for preprocessing every time. This section is the most important section.

Then, we will start the Machine learning theory section and a walkthrough of the Scikit-Learn Python package where we will learn how to write clean ML code. Thereafter, we will develop our first text classifier for SPAM and HAM message classification. I will also show you various types of word embeddings used in NLP like Bag of Words, Term Frequency, IDF, and TF-IDF. I will show you how you can estimate these features from scratch as well as with the help of the Scikit-Learn package.

Thereafter we will learn about the machine learning model deployment. We will also learn various other essential tools like word2vec, GloVe, Deep Learning, CNN, LSTM, RNN, etc.

Covered Keywords

Natural Language Processing, Python, Beginners, NLP, Text Processing, Text Analysis, Machine Learning, Data Science, Artificial Intelligence, Natural Language Understanding, Text Mining, Text Classification, Sentiment Analysis, Named Entity, Speech Recognition, Language Modeling, Text Generation, Text Summarization, Text Clustering, Text Similarity, Text Preprocessing, Regular Expressions, NLTK, spaCy, Gensim, Scikit-learn, TensorFlow, Keras, Numpy, Pandas, Jupyter Notebook, Data Visualization.

At the end of this lesson, you will learn everything which you need to solve your own NLP problem.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Machine Learning Intuition

Lecture 2: Course Overview

Lecture 3: DO NOT SKIP IT | Resources Folder!

Lecture 4: Install Anaconda and Python 3 on Windows 10

Lecture 5: Install Anaconda and Python 3 on Ubuntu Machine

Lecture 6: Install Anaconda and Python 3 on Mac Machine

Lecture 7: Install Git Bash and Commander Terminal

Lecture 8: Jupyter Notebook Shortcuts

Chapter 2: Python Crash Course

Lecture 1: Introduction

Lecture 2: Data Types

Lecture 3: Variable Assignment

Lecture 4: String Assignment

Lecture 5: List

Lecture 6: Set

Lecture 7: Tuple

Lecture 8: Dictionary

Lecture 9: Boolean and Comparison Operator

Lecture 10: Logical Operator

Lecture 11: If, Else, Elif

Lecture 12: Loops in Python

Lecture 13: Methods and Lambda Function

Chapter 3: Numpy Introduction [Optional]

Lecture 1: Introduction

Lecture 2: Array

Lecture 3: NaN and INF

Lecture 4: Statistical Operations

Lecture 5: Shape, Reshape, Ravel, Flatten

Lecture 6: Sequence, Repetitions, and Random Numbers

Lecture 7: Where(), ArgMax(), ArgMin()

Lecture 8: File Read and Write

Lecture 9: Concatenate and Sorting

Lecture 10: Working with Dates

Chapter 4: Pandas Introduction [Optional]

Lecture 1: Introduction

Lecture 2: DataFrame and Series

Lecture 3: File Reading and Writing

Lecture 4: Info, Shape, Duplicated, and Drop

Lecture 5: Columns

Lecture 6: NaN and Null Values

Lecture 7: Imputation

Lecture 8: Lambda Function

Chapter 5: Introduction of Spacy 3 for NLP

Lecture 1: Introduction to NLP

Lecture 2: Spacy 3 Introduction

Lecture 3: Spacy 3 Tokenization

Lecture 4: POS Tagging in Spacy 3

Lecture 5: Visualizing Dependency Parsing with Displacy

Lecture 6: Sentence Boundary Detection

Lecture 7: Stop Words in Spacy 3

Lecture 8: Lemmatization in Spacy 3

Lecture 9: Stemming in NLTK – Lemmatization vs Stemming in NLP

Lecture 10: Word Frequency Counter

Lecture 11: Rule Based Matching in Spacy Part 1

Lecture 12: Rule Based Token Matching Examples Part 2

Lecture 13: Rule Based Phrase Matching in Spacy

Lecture 14: Rule Based Entity Matching in Spacy

Lecture 15: NER (Named Entity Recognition) in Spacy 3 Part 1

Lecture 16: NER (Named Entity Recognition) in Spacy 3 Part 2

Lecture 17: Word to Vector (word2vec) and Sentence Similarity in Spacy

Lecture 18: Regular Expression Part 1

Lecture 19: Regular Expression Part 2

Chapter 6: Working with Text Files

Lecture 1: String Formatting

Lecture 2: Working with open() Files in write() Mode Part 1

Lecture 3: Working with open() Files in write() Mode Part 2

Lecture 4: Working with open() Files in write() Mode Part 3

Lecture 5: Read and Evaluate the Files

Lecture 6: Reading and Writing .CSV and .TSV Files with Pandas

Lecture 7: Reading and Writing .XLSX Files with Pandas

Lecture 8: Reading and Writing .JSON Files

Lecture 9: Reading Files from URL Links

Lecture 10: Extract Text Data From PDF

Lecture 11: Record the Audio and Convert to Text

Lecture 12: Convert Audio in Text Data

Lecture 13: Text to Speech Generation

Chapter 7: Complete Text Cleaning and Preprocessing

Lecture 1: Introduction

Lecture 2: Word Counts

Lecture 3: Characters Counts

Lecture 4: Average Word Length

Lecture 5: Stop Words Count

Lecture 6: Count #hashtag and @mentions

Lecture 7: Numeric Digit Count

Lecture 8: Upper case Words Count

Lecture 9: Lower case Conversion

Lecture 10: Contraction to Expansion

Lecture 11: Count and Remove Emails

Lecture 12: Count and Remove URLs

Lecture 13: Remove RT from Tweeter Data

Lecture 14: Special Chars Removal and Punctuation Removal

Lecture 15: Remove Multiple Spaces

Lecture 16: Remove HTML Tags

Lecture 17: Remove Accented Chars

Lecture 18: Remove Stop Words

Lecture 19: Convert into Base or Root Form of Words

Lecture 20: Common Words Removal

Lecture 21: Rare Words Removal

Lecture 22: Word Cloud Visualization

Instructors

Rating Distribution

  • 1 stars: 18 votes
  • 2 stars: 20 votes
  • 3 stars: 90 votes
  • 4 stars: 286 votes
  • 5 stars: 518 votes

Frequently Asked Questions


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You can view and review the lecture materials indefinitely, like an on-demand channel.


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