2024 Natural Language Processing in Python for Beginners

Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing

4.49 (722 reviews)
Udemy
platform
English
language
Data Science
category
18,493
students
30 hours
content
Mar 2024
last update
$79.99
regular price

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.

Description

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.

Content

Introduction

Machine Learning Intuition
Install Anaconda and Python 3 on Windows 10
Resources Folder
Install Anaconda and Python 3 on Ubuntu Machine
Install Git Bash and Commander Terminal
Jupyter Notebook Shortcuts

Python Crash Course

Introduction
Data Types
Variable Assignment
String Assignment
List
Set
Tuple
Dictionary
Boolean and Comparison Operator
Logical Operator
If, Else, Elif
Loops in Python
Methods and Lambda Function

Numpy Introduction [Optional]

Introduction
Array
NaN and INF
Statistical Operations
Shape, Reshape, Ravel, Flatten
Sequence, Repetitions, and Random Numbers
Where(), ArgMax(), ArgMin()
File Read and Write
Concatenate and Sorting
Working with Dates

Pandas Introduction [Optional]

Introduction
DataFrame and Series
File Reading and Writing
Info, Shape, Duplicated, and Drop
Columns
NaN and Null Values
Imputation
Lambda Function

Spacy Introduction

Introduction to NLP
Install Spacy
Introduction to Spacy
Tokenization
Parts of Speech [POS] Tagging
Dependency Visualization
Named Entity Recognition (NER)
Sentence Segmentation
Rule Based Phrase Matching
Regular Expression Part 1
Regular Expression Part 2
Processing Pipeline in Spacy
Hashtags and Emoji Detection

Working with Text Files

String Formatting
Working with open() Files in write() Mode Part 1
Working with open() Files in write() Mode Part 2
Working with open() Files in write() Mode Part 3
Read and Evaluate the Files
Reading and Writing .CSV and .TSV Files with Pandas
Reading and Writing .XLSX Files with Pandas
Reading and Writing .JSON Files
Reading Files from URL Links
Extract Text Data From PDF
Record the Audio and Convert to Text
Convert Audio in Text Data
Text to Speech Generation

Complete Text Preprocessing

Introduction
Word Counts
Characters Counts
Average Word Length
Stop Words Count
Count #hashtag and @mentions
Numeric Digit Count
Upper case Words Count
Lower case Conversion
Contraction to Expansion
Count and Remove Emails
Count and Remove URLs
Remove RT from Tweeter Data
Special Chars Removal and Punctuation Removal
Remove Multiple Spaces
Remove HTML Tags
Remove Accented Chars
Remove Stop Words
Convert into Base or Root Form of Words
Common Words Removal
Rare Words Removal
Word Cloud Visualization
Spelling Correction
Tokenization with TextBlob
Nouns Detection
Language Translation and Detection
Sentiment Prediction with TextBlob

Python Software Packaging for Redistribution

Code Files Setup
Readme and License File Preparation
Setup.py Preparation
Utils.py Code Along Part 1
Utils.py Code Along Part 2
Utils.py Code Along Part 3
Utils.py Code Along Part 4
__init__.py Code Along
GitHub Account Setup and Package Upload
SSH Key Setup for GitHub
Install Preprocess Python Package
Removing the Errors Part 1
Removing the Errors Part 2
Testing the Package

Introduction to Machine Learning with Scikit-Learn

Logistic Regression Intuition
Support Vector Machine Intuition
Decision Tree Intuition
Random Forest Intuition
L2 Regularization
L1 Regularization
Model Evaluation Metrics: Accuracy, Precision, Recall, and Confusion Matrix
Model Evaluation Metrics: ROC and AUC
Code Along in Python Part 1
Code Along in Python Part 2
Code Along in Python Part 3
Code Along in Python Part 4

Your First Text Classifier | Spam Text Classification

Text Feature Extraction Intuition Part 1
Text Feature Extraction Intuition Part 2
Bag of Words (BoW) Code Along in Python
Term Frequency (TF) Code Along in Python
Inverse Document Frequency (IDF) Code Along in Python
TFIDF Code Along in Python
Load Spam Dataset
Balance Dataset
Exploratory Data Analysis (EDA)
Data Preparation for Training
Build and Train SVM and Random Forest Models
Test Your Model with Real Data

Real-Time Twitter Sentiment Analysis

Notebook Setup
SVM Model Training
Test Your Model
Data Cleaning and Retraining SVM Part 1
Data Cleaning and Retraining SVM Part 2
Fine Tune Your ML Model
Saving and Loading ML Model
Create Twitter Developer Account
Get the Access Tokens
Reading Twitter Timeline in Real-Time
Tracking Keywords in Real-Time on Twitter Part 1
Tracking Keywords in Real-Time on Twitter Part 2
Tracking Keywords in Real-Time on Twitter Part 3
Real-Time Sentiment Analysis with TextBlob
Real-Time Sentiment Analysis with Trained ML Model
Real-Time Twitter Sentiment Analysis of USA vs China Part 1
Real-Time Twitter Sentiment Analysis of USA vs China Part 2
Real-Time Twitter Sentiment Animation Plot Part 1
Real-Time Twitter Sentiment Animation Plot Part 2

Screenshots

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Reviews

Ritik
September 22, 2023
it seems exciting to learn the concepts and real time projects at the end of every section of the concpts to be covered.
Emeka
September 16, 2023
This is a really good course with lot of high value content but sometimes i do struggle with the instructor's English accent. Aside from that, this course is worth it.
Subhajit
July 4, 2023
The course is very good in itself and is detailed, concise and yet informative. The only problem is his English. I mean you can understand it but its such a pain.
Harisri
June 5, 2023
The instructor seems to be having good knowledge in the field. However, the pronunciation of words and stumbling upon what to say next makes the audio very hard to follow.
Parthena
May 12, 2023
Exactly what I needed regarding content. I just have difficulty understanding what has been said at some points because of the teacher's accent.
Beatrix
May 9, 2023
It is rather a collection of tutorials and not a fully balanced course which gives memorable insights. Explanations are not deep enough for me personally, I had to do a lot of googling and chatgpting besides the course. The models are trained with very small dataset which results in poor accuracy.. which makes me doubt if I can use this knowledge for work.. I got a fair overview about libraries and nlp tools.. also at the beginning i liked the in depth eplanation about text embeddings. but overall, what i learned here is "throw any nlp model at it and see if it works..." . Also, please improve on the explanations about matrix/vector transformations and why you do them... i could not follow.
Edvaldo
April 14, 2023
That's a great course. The lecturer have a huge knowledge about the content and give us the privilege of knowing the specific things about each problem, model and approach. Just an awesome course.
Rahul
March 1, 2023
Giving 3 start because they have stopped bothering about their material. They keep on using the dataset from their GIT repo but those are not accessible anymore. You cannot do practice in the code along part. However explanation of logic is good.
Deepak
February 26, 2023
I gave 4.5 stars just for the amount of topics that the instructor has covered in this course and the effort he put in designing this. But the explanations aren't satisfactory for any beginner. The python codes can be used for future purposes but I feel one should take other courses or study materials to understand the theory better.
Carl
January 14, 2023
Very mixed. I'm not a beginner but I struggles to follow much of the course because the instructor was just typing code (badly) and didn't really stand back and give the big picture of what he was trying to communicate. There were some nuggets though.
Aaron
August 2, 2022
I would give 5 star if Udemy allowed screenshots and if there were accompanying resources for relevant sections such as the string formatting in one of the previous section (took me a long time to copy it) or the twitter data used in another lecture.
tanmay
February 26, 2022
i really love this course, mentor has started from the beginning and covered all the topic soo much in depth. at every moment it is like jurasic park movie which keep me thrilling and enthusisatic.
Jenny
November 18, 2021
This is the first time I bought class on Udemy, honestly ,quite disappointed. 1) Not enough infomation, with a lot of contents repeating similar topics, which only cover a very very little scenario of NLP 2) Instructor did not give instant answer, I asked one question regarding original coding issue but got no reply after one month.
Jacob
September 15, 2021
My favorite part is the very well-organized notebooks that show you everything he's explaining in the videos. I feel like this will be a fantastic reference guide for me as I learn NLP.
Eli
September 14, 2021
I liked this course. The topics were very relevant. The lessons were neither too long nor too short. The notebooks are available and clean. Great course.

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3257648
udemy ID
6/21/2020
course created date
7/17/2020
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