2023 Natural Language Processing In Python For Beginners

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

What you'll 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.

Requirements

Have a desire to learn

Elementary level math

Have basic understanding of Python and Machine Learning

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 KeywordsNatural 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.

Overview

Section 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

Section 2: Python Crash Course

Lecture 9 Introduction

Lecture 10 Data Types

Lecture 11 Variable Assignment

Lecture 12 String Assignment

Lecture 13 List

Lecture 14 Set

Lecture 15 Tuple

Lecture 16 Dictionary

Lecture 17 Boolean and Comparison Operator

Lecture 18 Logical Operator

Lecture 19 If, Else, Elif

Lecture 20 Loops in Python

Lecture 21 Methods and Lambda Function

Section 3: Numpy Introduction [Optional]

Lecture 22 Introduction

Lecture 23 Array

Lecture 24 NaN and INF

Lecture 25 Statistical Operations

Lecture 26 Shape, Reshape, Ravel, Flatten

Lecture 27 Sequence, Repetitions, and Random Numbers

Lecture 28 Where(), ArgMax(), ArgMin()

Lecture 29 File Read and Write

Lecture 30 Concatenate and Sorting

Lecture 31 Working with Dates

Section 4: Pandas Introduction [Optional]

Lecture 32 Introduction

Lecture 33 DataFrame and Series

Lecture 34 File Reading and Writing

Lecture 35 Info, Shape, Duplicated, and Drop

Lecture 36 Columns

Lecture 37 NaN and Null Values

Lecture 38 Imputation

Lecture 39 Lambda Function

Section 5: Introduction of Spacy 3 for NLP

Lecture 40 Introduction to NLP

Lecture 41 Spacy 3 Introduction

Lecture 42 Spacy 3 Tokenization

Lecture 43 POS Tagging in Spacy 3

Lecture 44 Visualizing Dependency Parsing with Displacy

Lecture 45 Sentence Boundary Detection

Lecture 46 Stop Words in Spacy 3

Lecture 47 Lemmatization in Spacy 3

Lecture 48 Stemming in NLTK - Lemmatization vs Stemming in NLP

Lecture 49 Word Frequency Counter

Lecture 50 Rule Based Matching in Spacy Part 1

Lecture 51 Rule Based Token Matching Examples Part 2

Lecture 52 Rule Based Phrase Matching in Spacy

Lecture 53 Rule Based Entity Matching in Spacy

Lecture 54 NER (Named Entity Recognition) in Spacy 3 Part 1

Lecture 55 NER (Named Entity Recognition) in Spacy 3 Part 2

Lecture 56 Word to Vector (word2vec) and Sentence Similarity in Spacy

Lecture 57 Regular Expression Part 1

Lecture 58 Regular Expression Part 2

Section 6: Working with Text Files

Lecture 59 String Formatting

Lecture 60 Working with open() Files in write() Mode Part 1

Lecture 61 Working with open() Files in write() Mode Part 2

Lecture 62 Working with open() Files in write() Mode Part 3

Lecture 63 Read and Evaluate the Files

Lecture 64 Reading and Writing .CSV and .TSV Files with Pandas

Lecture 65 Reading and Writing .XLSX Files with Pandas

Lecture 66 Reading and Writing .JSON Files

Lecture 67 Reading Files from URL Links

Lecture 68 Extract Text Data From PDF

Lecture 69 Record the Audio and Convert to Text

Lecture 70 Convert Audio in Text Data

Lecture 71 Text to Speech Generation

Section 7: Complete Text Cleaning and Preprocessing

Lecture 72 Introduction

Lecture 73 Word Counts

Lecture 74 Characters Counts

Lecture 75 Average Word Length

Lecture 76 Stop Words Count

Lecture 77 Count #hashtag and @mentions

Lecture 78 Numeric Digit Count

Lecture 79 Upper case Words Count

Lecture 80 Lower case Conversion

Lecture 81 Contraction to Expansion

Lecture 82 Count and Remove Emails

Lecture 83 Count and Remove URLs

Lecture 84 Remove RT from Tweeter Data

Lecture 85 Special Chars Removal and Punctuation Removal

Lecture 86 Remove Multiple Spaces

Lecture 87 Remove HTML Tags

Lecture 88 Remove Accented Chars

Lecture 89 Remove Stop Words

Lecture 90 Convert into Base or Root Form of Words

Lecture 91 Common Words Removal

Lecture 92 Rare Words Removal

Lecture 93 Word Cloud Visualization

Lecture 94 Spelling Correction

Lecture 95 Tokenization with TextBlob

Lecture 96 Nouns Detection

Lecture 97 Language Translation and Detection

Lecture 98 Sentiment Prediction with TextBlob

Section 8: Text Cleaning and Preprocessing in Python | Software Packaging for PIP Install

Lecture 99 Code Files Setup

Lecture 100 Readme and License File Preparation

Lecture 101 Setup.py Preparation

Lecture 102 Utils.py Code Along Part 1

Lecture 103 Utils.py Code Along Part 2

Lecture 104 Utils.py Code Along Part 3

Lecture 105 Utils.py Code Along Part 4

Lecture 106 __init__.py Code Along

Lecture 107 GitHub Account Setup and Package Upload

Lecture 108 SSH Key Setup for GitHub

Lecture 109 Install Preprocess Python Package

Lecture 110 Removing the Errors Part 1

Lecture 111 Removing the Errors Part 2

Lecture 112 Testing the Package

Section 9: Introduction to Machine Learning with Scikit-Learn

Lecture 113 Logistic Regression Intuition

Lecture 114 Support Vector Machine Intuition

Lecture 115 Decision Tree Intuition

Lecture 116 Random Forest Intuition

Lecture 117 L2 Regularization

Lecture 118 L1 Regularization

Lecture 119 Model Evaluation Metrics: Accuracy, Precision, Recall, and Confusion Matrix

Lecture 120 Model Evaluation Metrics: ROC and AUC

Lecture 121 Code Along in Python Part 1

Lecture 122 Code Along in Python Part 2

Lecture 123 Code Along in Python Part 3

Lecture 124 Code Along in Python Part 4

Section 10: Spam Text Classification

Lecture 125 Text Feature Extraction Intuition Part 1

Lecture 126 Text Feature Extraction Intuition Part 2

Lecture 127 Bag of Words (BoW) Code Along in Python

Lecture 128 Term Frequency (TF) Code Along in Python

Lecture 129 Inverse Document Frequency (IDF) Code Along in Python

Lecture 130 TFIDF Code Along in Python

Lecture 131 Load Spam Dataset

Lecture 132 Balance Dataset

Lecture 133 Exploratory Data Analysis (EDA)

Lecture 134 Data Preparation for Training

Lecture 135 Build and Train SVM and Random Forest Models

Lecture 136 Test Your Model with Real Data

Section 11: Real-Time Twitter Sentiment Analysis

Lecture 137 Notebook Setup

Lecture 138 SVM Model Training

Lecture 139 Test Your Model

Lecture 140 Data Cleaning and Retraining SVM Part 1

Lecture 141 Data Cleaning and Retraining SVM Part 2

Lecture 142 Fine Tune Your ML Model

Lecture 143 Saving and Loading ML Model

Lecture 144 Create Twitter Developer Account

Lecture 145 Get the Access Tokens

Lecture 146 Reading Twitter Timeline in Real-Time

Lecture 147 Tracking Keywords in Real-Time on Twitter Part 1

Lecture 148 Tracking Keywords in Real-Time on Twitter Part 2

Lecture 149 Tracking Keywords in Real-Time on Twitter Part 3

Lecture 150 Real-Time Sentiment Analysis with TextBlob

Lecture 151 Real-Time Sentiment Analysis with Trained ML Model

Lecture 152 Real-Time Twitter Sentiment Analysis of USA vs China Part 1

Lecture 153 Real-Time Twitter Sentiment Analysis of USA vs China Part 2

Lecture 154 Real-Time Twitter Sentiment Animation Plot Part 1

Lecture 155 Real-Time Twitter Sentiment Animation Plot Part 2

Section 12: Fine Tuning of ML Algorithms

Lecture 156 What is Feature Dimensionality Reduction

Lecture 157 Principal Components Analysis (PCA)

Lecture 158 Linear Discriminant Analysis (LDA)

Lecture 159 Non-Negative Matrix Factorization (NMF)

Lecture 160 Truncated Singular Value Decomposition (TSVD)

Lecture 161 TF-IDF and Sparse Matrix Part 1

Lecture 162 TF-IDF and Sparse Matrix Part 2

Lecture 163 TF-IDF and Sparse Matrix Part 3

Lecture 164 Non-Negative Matrix Factorization (NMF) Code Along Part 1

Lecture 165 Non-Negative Matrix Factorization (NMF) Code Along Part 2

Lecture 166 Truncated Singular Value Decomposition (TSVD) Code Along

Lecture 167 What is Hyperparameters Tuning

Lecture 168 Hyperparameter Tuning Methods

Lecture 169 Grid Search for Hyperparameters with K-Fold Cross-Validation

Lecture 170 GridSearch for Logistic Regression Hyperparameters Tuning Part 1

Lecture 171 GridSearch for Logistic Regression Hyperparameters Tuning Part 2

Lecture 172 GridSearch for SVM Hyperparameters Tuning Part 1

Lecture 173 GridSearch for SVM Hyperparameters Tuning Part 2

Lecture 174 Grid Search for Random Forest Classifier Hyperparameters Tuning

Lecture 175 Random Search for Best Hyperparameters Selection

Lecture 176 Selecting Best Models from Multiple ML Algorithms

Section 13: Sentiment Analysis on IMDB Movie Reviews with TF-IDF Text Embedding

Lecture 177 How Sentiment is Detected from Text Data

Lecture 178 Text Preprocessing Package Install

Lecture 179 Text Cleaning and Preprocessing

Lecture 180 Data Preparation for Model Training

Lecture 181 ML Model Building and Training

Lecture 182 Logistic Regression Model Evaluation

Lecture 183 Traning and Hyperparameters Tuning of SVM

Lecture 184 Load and Store ML Model

Section 14: ML Model Deployment with Flask

Lecture 185 Install Flask

Lecture 186 Run Flask Server

Lecture 187 Model Preparation with Flask

Lecture 188 Running Flask App with ML Model Part 1

Lecture 189 Running Flask App with ML Model Part 2

Section 15: Multi-Label Text Classification for Stack Overflow Tag Prediction

Lecture 190 Getting Familiar with Data

Lecture 191 What is Multi-Label Classification

Lecture 192 Loading Dataset

Lecture 193 Multi-Label Binarization

Lecture 194 Text to TFIDF Vectors

Lecture 195 Model Building and Jaccard Score

Lecture 196 Improving and Saving the Model

Section 16: Sentiment Analysis using Word2Vec Text Embedding

Lecture 197 What is word2vec

Lecture 198 How to Get word2vec

Lecture 199 Word Vectors with Spacy

Lecture 200 Semantic Similarity with Spacy

Lecture 201 Data Preparation

Lecture 202 Data Preprocessing

Lecture 203 Get word2vec from DataFrame

Lecture 204 Split Dataset in Train and Test

Lecture 205 ML Model Traning and Testing

Lecture 206 Support Vector Machine on word2vec

Lecture 207 Grid Search Cross Validation for Hyperparameters Tuning

Lecture 208 Test Every Machine Learning Model

Section 17: Emotion Recognition in Text Data using GloVe Vectors Text Embedding

Lecture 209 What is GloVe Vectors Part 1

Lecture 210 What is GloVe Vectors Part 2

Lecture 211 Download Pre-trained GloVe Vectors

Lecture 212 Data Preparation

Lecture 213 Preprocessing and Cleaning of Emotion Text Data

Lecture 214 Load GloVe Vector

Lecture 215 Text to GloVe Vectors

Lecture 216 Text to GloVe on Pandas DataFrame

Lecture 217 ML Model Training and Testing

Lecture 218 Support Vector Machine for Emotion Recognition

Lecture 219 Predict Text Emotion with Custom Data

Section 18: Resume (CV) Parsing using Spacy 3

Lecture 220 Resume (CV) Parsing Introduction

Lecture 221 NER Training Introduction and Config Setup

Lecture 222 NER Training Data Preparation

Lecture 223 Training Configuration File Explanation

Lecture 224 NER Training Data Preparation Part 1

Lecture 225 NER Training Data Preparation Part 2

Lecture 226 NER Training with Transformers

Lecture 227 CV Parsing and NER Prediction

Section 19: Sentiment Analysis using Deep Learning

Lecture 228 What is Deep Learning?

Lecture 229 What Makes Deep Learning State-of-the-Art?

Lecture 230 How Deep Learning Works?

Lecture 231 Types of Neural Networks in Deep Learning - ANN

Lecture 232 Types of Neural Networks in Deep Learning - CNN

Lecture 233 How Deep Learning Learns?

Lecture 234 What is the Difference Between Deep Learning and Machine Learning?

Lecture 235 Build ANN - Steps for Building Your First Model

Lecture 236 Python Package Installation

Lecture 237 Data Preprocessing

Lecture 238 Get the word2vec

Lecture 239 Train Test and Split

Lecture 240 Feature Standardization

Lecture 241 ANN Model Building and Training

Lecture 242 Confusion Matrix Plot

Lecture 243 Setting Custom Threshold

Lecture 244 1D CNN Model Building and Training

Lecture 245 Plot Learning Curve

Lecture 246 Model Load, Store and Testing

Section 20: Hate Speech Classification | Multi-Class Classification with CNN

Lecture 247 Hate Speech Classification Introduction?

Lecture 248 Import Python Package

Lecture 249 Dataset Balancing

Lecture 250 Text Preprocessing

Lecture 251 Text Tokenization

Lecture 252 Train Test and Split

Lecture 253 Build and Train CNN

Lecture 254 Model Testing

Lecture 255 Testing with Custom Data

Lecture 256 Load Store Model

Section 21: Poetry Generation Using Tensorflow, Keras, and LSTM

Lecture 257 Introduction to Reccurent Neural Network (RNN)

Lecture 258 Types of RNN

Lecture 259 The Problem of RNN's or Long-Term Dependencies

Lecture 260 Long Short Term Memory (LSTM) Networks

Lecture 261 Sequence Generation Scheme

Lecture 262 Loading Poetry Dataset

Lecture 263 Tokenization

Lecture 264 Prepare Training Data

Lecture 265 Padding

Lecture 266 LSTM Model Training

Lecture 267 Poetry Generation Part 1

Lecture 268 Poetry Generation Part 2

Section 22: Disaster Tweets Classification using Deep Learning Word Embeddings

Lecture 269 Disaster Tweets Dataset Understanding

Lecture 270 Download Dataset

Lecture 271 Target Class Distribution

Lecture 272 Number of Characters Distribution in Tweets

Lecture 273 Number of Words, Average Words Length, and Stop words Distribution in Tweets

Lecture 274 Most and Least Common Words

Lecture 275 One-Shot Data Cleaning

Lecture 276 Disaster Words Visualization with Word Cloud

Lecture 277 Classification with TF-IDF and SVM

Lecture 278 Prediction on Test Data

Lecture 279 Classification with Word2Vec and SVM

Lecture 280 Word Embeddings and Classification with Deep Learning Part 1

Lecture 281 Word Embeddings and Classification with Deep Learning Part 2

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