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Natural Language Processing

(CTU-AI323.AJ1) / ISBN : 979-8-90059-940-3
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1

Statistical Foundations of NLP

  • What is NumPy?
  • What are NumPy Arrays?
  • Lists and Exponents
  • Arrays and Exponents
  • Calculating the Mean and Standard Deviation
  • Plotting a Line with NumPy and Matplotlib
  • What is Linear Regression?
  • The MSE Formula
  • What is Pandas?
  • A Pandas Data Frame with NumPy Example
  • Describing a Pandas Data Frame
  • Reading CSV Files in Pandas
  • The loc() and iloc() Methods in Pandas
  • Converting Categorical Data to Numeric Data
  • Combining Pandas Data frames
  • Data Manipulation with Pandas Data Frames (1)
  • Data Manipulation with Pandas Data Frames (2)
  • Handling Missing Data in Pandas
  • Sorting Data Frames in Pandas
  • Working with groupby() in Pandas
  • Pandas Data Frames and Simple Statistics
  • Aggregate Operations in Pandas Data Frames
  • Working with JSON-based Data
  • What is Text Encoding?
  • Text Encoding Techniques
  • The BoW Algorithm
  • What are n-grams?
  • Calculating tf, idf, and tf-idf
  • The Context of Words in a Document
  • What is Cosine Similarity?
  • Text Vectorization (aka Word Embeddings)
  • Overview of Word Embeddings and Algorithms
  • What is Word2vec?
  • The CBoW Architecture
  • What are Skip-grams?
  • What is GloVe?
  • Comparison of Word Embeddings
  • Language Models and NLP
2

Machine Learning for NLP Tasks

  • Cleaning Data with Regular Expressions
  • Handling Contracted Words
  • Python Code Samples of BoW
  • One-Hot Encoding Examples
  • Sklearn and Word Embedding Examples
  • Web Scraping with Pure Regular Expressions
  • What is SpaCy?
  • What is NLTK?
  • NLTK and BoW
  • NLTK and Stemmers
  • NLTK and Lemmatization
  • NLTK and Stop Words
  • What is Wordnet?
  • NLTK and n-grams
  • NLTK and POS (1)
  • NLTK and POS (2)
  • NLTK and Tokenizers
  • What is Gensim?
  • An Example of Topic Modeling
  • A Brief Comparison of Popular Python-Based NLP Libraries
  • What is Classification?
  • What are Linear Classifiers?
  • What is kNN?
  • What are Decision Trees?
  • Decision Trees, Gini Impurity, and Entropy
  • What are Random Forests?
  • What are Support Vector Machines?
  • What is a Bayesian Classifier?
  • Training Classifiers
  • Evaluating Classifiers
  • Trade-offs for ML Algorithms
  • What are Activation Functions?
  • Common Activation Functions
  • The ReLU and ELU Activation Functions
  • Sigmoid, Softmax, and Hardmax Similarities
  • Hyperparameters for Neural Networks
  • What is Logistic Regression?
3

NLP Applications Across Domains

  • What is Machine Learning?
  • Types of Machine Learning Algorithms
  • Preparing a Dataset and Training a Model
  • Feature Engineering, Selection, and Extraction
  • Working with Datasets
  • Overfitting versus Underfitting
  • Data Normalization Techniques
  • Metrics in Machine Learning
  • What is Linear Regression?
  • Other Types of Regression
  • The Mean Squared Error (MSE) Formula
  • Calculating the MSE Manually
  • What are Ensemble Methods?
  • Four Types of Ensemble Methods
  • Common Boosting Algorithms
  • Hyperparameter Optimization
  • AutoML, AutoML-Zero, and AutoNLP
  • What is Text Summarization?
  • Text Summarization with gensim and SpaCy
  • What are Recommender Systems?
  • Content-Based Recommendation Systems
  • Collaborative Filtering Algorithm
  • What is Sentiment Analysis?
  • Sentiment Analysis with Naïve Bayes
  • Sentiment Analysis in NLTK and VADER
  • Sentiment Analysis with Textblob
  • Sentiment Analysis with Flair
  • Detecting Spam
  • Logistic Regression and Sentiment Analysis
  • What are Chatbots?
4

Performance Optimization in NLP Systems

  • Term-Document Matrix
  • Text Classification Algorithms in Machine Learning
  • A Keras-Based Tokenizer
  • TF2 and Tokenization
  • TF2 and Encoding
  • A Keras-Based Word Embedding
  • An Example of BoW with TF2
  • The 20newsgroup Dataset
  • Text Classification with the kNN Algorithm
  • Text Classification with a Decision Tree Algorithm
  • Text Classification with a Random Forest Algorithm
  • Text Classification with the SVC Algorithm
  • Text Classification with the Naïve Bayes Algorithm
  • Text Classification with the kMeans Algorithm
  • TF2/Keras and Word Tokenization
  • TF2/Keras and Word Encodings
  • Text Summarization with TF2/Keras and Reuters Dataset
5

Research and Emerging Trends in NLP

  • What is Attention?
  • An Overview of the Transformer Architecture
  • What is T5?
  • What is BERT?
  • The Inner Workings of BERT
  • Subword Tokenization
  • Sentence Similarity in BERT
  • Generating BERT Tokens (1)
  • Generating BERT Tokens (2)
  • The BERT Family
  • Introduction to GPT
  • Working with GPT-2
  • What is GPT-3?
  • The Switch Transformer: One Trillion Parameters
  • Looking Ahead
A

Appendix A: Data and Statistics

  • What are Datasets?
  • Preparing Datasets
  • Missing Data, Anomalies, and Outliers
  • What is Imbalanced Classification?
  • What is SMOTE?
  • Analyzing Classifiers
  • What is a Probability?
  • Random Variables
  • Fundamental Concepts in Statistics
  • The Moments of a Function (Optional)
  • Data and Statistics
  • The Bias-Variance Trade-off
  • Gini Impurity, Entropy, and Perplexity
  • Cross-Entropy and KL Divergence
  • Covariance and Correlation Matrices
  • Principal Component Analysis (PCA)
  • Dimensionality Reduction
  • Dimensionality Reduction Techniques
  • Linear Versus Nonlinear Reduction Techniques
  • Types of Distance Metrics
  • Other Well-Known Distance Metrics
  • What is Sklearn?
  • What is Bayesian Inference?
  • What are Vector Spaces?
B

Appendix B: Introduction to Python

  • Tools for Python
  • Python Installation
  • Setting the PATH Environment Variable (Windows Only)
  • Launching Python on Your Machine
  • Python Identifiers
  • Lines, Indentation, and Multilines
  • Quotation and Comments in Python
  • Saving Your Code in a Module
  • Some Standard Modules in Python
  • The help() and dir() Functions
  • Compile Time and Runtime Code Checking
  • Simple Data Types in Python
  • Working with Numbers
  • Working with Fractions
  • Unicode and UTF-8
  • Working with Unicode
  • Working with Strings
  • Uninitialized Variables and the Value None in Python
  • Slicing and Splicing Strings
  • Search and Replace a String in Other Strings
  • Remove Leading and Trailing Characters
  • Printing Text without NewLine Characters
  • Text Alignment
  • Working with Dates
  • Exception Handling in Python
  • Handling User Input
  • Python and Emojis (Optional)
  • Command-Line Arguments
C

Appendix C: Introduction to Regular Expressions

  • What are Regular Expressions?
  • Metacharacters in Python
  • Character Sets in Python
  • Character Classes in Python
  • Matching Character Classes with the re Module
  • Using the re.match() Method
  • Options for the re.match() Method
  • Matching Character Classes with the re.search() Method
  • Matching Character Classes with the findAll() Method
  • Additional Matching Function for Regular Expressions
  • Grouping with Character Classes in Regular Expressions
  • Using Character Classes in Regular Expressions
  • Modifying Text Strings with the re Module
  • Splitting Text Strings with the re.split() Method
  • Splitting Text Strings Using Digits and Delimiters
  • Substituting Text Strings with the re.sub() Method
  • Matching the Beginning and the End of Text Strings
  • Compilation Flags
  • Compound Regular Expressions
  • Counting Character Types in a String
  • Regular Expressions and Grouping
  • Simple String Matches
  • Additional Topics for Regular Expressions
D

Appendix D: Introduction to Keras

  • What is Keras?
  • Creating a Keras-Based Model
  • Keras and Linear Regression
  • Keras, MLPs, and MNIST
  • Keras, CNNs, and cifar10
  • Resizing Images in Keras
  • Keras and Early Stopping (1)
  • Keras and Early Stopping (2)
  • Keras and Metrics
  • Saving and Restoring Keras Models
E

Appendix E: Introduction to TensorFlow 2

  • What is TF 2?
  • Other TF 2-Based Toolkits
  • TF 2 Eager Execution
  • TF 2 Tensors, Data Types, and Primitive Types
  • Constants in TF 2
  • Variables in TF 2
  • The tf.rank() API
  • The tf.shape() API
  • Variables in TF 2 (Revisited)
  • What is @tf.function in TF 2?
  • Working with @tf.function in TF 2
  • Arithmetic Operations in TF 2
  • Caveats for Arithmetic Operations in TF 2
  • TF 2 and Built-In Functions
  • Calculating Trigonometric Values in TF 2
  • Calculating Exponential Values in TF 2
  • Working with Strings in TF 2
  • Working with Tensors and Operations in TF 2
  • Second-Order Tensors in TF 2 (1)
  • Second-Order Tensors in TF 2 (2)
  • Multiplying Two Second-Order Tensors in TF
  • Convert Python Arrays to TF Tensors
  • Differentiation and tf.GradientTape in TF 2
  • Examples of tf.GradientTape
  • What is Trax?
  • Google Colaboratory
  • Other Cloud Platforms
  • TF2 and tf.data.Dataset
  • The TF 2 tf.data.Dataset
  • What are Lambda Expressions?
  • Working with Generators in TF 2
F

Appendix F: Data Visualization

  • What is Data Visualization?
  • What is Matplotlib?
  • Horizontal Lines in Matplotlib
  • Slanted Lines in Matplotlib
  • Parallel Slanted Lines in Matplotlib
  • A Grid of Points in Matplotlib
  • A Dotted Grid in Matplotlib
  • Lines in a Grid in Matplotlib
  • A Colored Grid in Matplotlib
  • A Colored Square in an Unlabeled Grid in Matplotlib
  • Randomized Data Points in Matplotlib
  • A Histogram in Matplotlib
  • A Set of Line Segments in Matplotlib
  • Plotting Multiple Lines in Matplotlib
  • Trigonometric Functions in Matplotlib
  • Display IQ Scores in Matplotlib
  • Plot a Best-Fitting Line in Matplotlib
  • Introduction to Sklearn (scikit-learn)
  • The Digits Dataset in Sklearn
  • The Iris Dataset in Sklearn
  • The Iris Dataset in Sklearn (Optional)
  • The faces Dataset in Sklearn (Optional)
  • Working with Seaborn
  • Seaborn Built-in Datasets
  • The Iris Dataset in Seaborn
  • The Titanic Dataset in Seaborn
  • Extracting Data from the Titanic Dataset in Seaborn (1)
  • Extracting Data from the Titanic Dataset in Seaborn (2)
  • Visualizing a Pandas Dataset in Seaborn
  • Data Visualization in Pandas

1

Statistical Foundations of NLP

  • Using Lists and Arrays
  • Performing Statistical Operations
  • Creating Line Charts
  • Performing Linear Regression
  • Creating and Accessing DataFrames
  • Working with Data Frames - I
  • Working with Data Frames - II
  • Analyzing JSON Data Structures
2

Machine Learning for NLP Tasks

  • Cleaning Data and Handling Contracted Words
  • Analyzing Encoding and Embedding Techniques
  • Performing Web Scraping
  • Using Gensim for Text Processing
  • Using NLTK for Text Processing
  • Selecting the Best Classification Model
3

NLP Applications Across Domains

  • Performing Data Normalization Using scikit-learn
  • Plotting and Interpreting ROC Curves and AUC Scores
  • Performing Sentiment Analysis Using NLTK and the Multinomial Naïve Bayes Classifier
  • Evaluating Regression Models Using Error Metrics
  • Summarizing Text with Gensim and spaCy
  • Creating a Spam Classifier
4

Performance Optimization in NLP Systems

  • Preparing Text Data for Deep Learning-Based NLP Models
  • Creating Word Embeddings Using Keras Neural Networks for Text Classification
  • Implementing BoW Text Representation Using Tensorflow/Keras
  • Performing Text Processing Using TF2 and Keras
  • Comparing Text Classification Models
  • Performing Text Tokenization and N-gram Extraction Using TensorFlow Text
5

Research and Emerging Trends in NLP

  • Using the transformers Library
  • Performing NER Using Hugging Face Transformers
  • Performing Subword Tokenization Using WordPiece And BPE
  • Generating BERT Tokens
  • Performing Text Generation Using GPT-2
  • Analyzing GPT Models

Natural Language Processing

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