Natural Language Processing and Machine Learning for Developers

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Title
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Contents
Preface
Chapter 1 Introduction of NumPy
	What is NumPy?
		Useful NumPy Features
	What are NumPy Arrays?
	Working with Loops
	Appending Elements to Arrays (1)
	Appending Elements to Arrays (2
)
	Multiply Lists and Arrays
	Doubling the Elements in a List
	Lists and Exponents
	Arrays and Exponents
	Math Operations and Arrays
		Working with “-1” Subranges with Vectors
	Working with “-1” Subranges with Arrays
	Other Useful NumPy Methods
	Arrays and Vector Operations
	NumPy and Dot Products (1)
	NumPy and Dot Products (2
)
	NumPy and the “Norm” of Vectors
	NumPy and Other Operations
	NumPy and the reshape() Method
	Calculating the Mean and Standard Deviation
		Trimmed Mean and Weighted Mean
		Code Sample with Mean and Standard Deviation
	Working with Lines in the Plane (Optional)
	Plotting a Line with NumPy and Matplotlib
	Plotting a Quadratic with NumPy and Matplotlib
	What is Linear Regression?
		What i
s Multivariate Analysis?
		What about Nonlinear Datasets?
	The MSE Formula
		Other Error Types
		Nonlinear Least Squares
	Calculating the MSE Manually
	Find the Best-Fitting Line with NumPy
	Calculating MSE by Successive Approximation (1)
	Calculating MSE by Successive Approximation (2
)
	What is Jax?
	Google Colaboratory
		Uploading CSV Files in Google Colaboratory
	Summary
Chapter 2 Introduction to Pandas
	What is Pandas?
		Pandas Options and Settings
		Pandas Data Frames
		Data Frames and Data Cleaning Tasks
		Alternatives to Pandas
	A Pandas Data Frame with NumPy Example
	Describing a Pandas Data Frame
	Pandas Boolean Data Frames
		Transposing a Pandas Data Frame
	Pandas Data Frames and Random Numbers
	Reading CSV Files in Pandas
	The loc() and iloc() Methods in Pandas
	Converting Categorical Data to Numeric Data
	Matching and Splitting Strings in Pandas
	Converting Strings to Dates in Pandas
	Merging and Splitting Columns in Pandas
	Combining Pandas Data frames
	Data Manipulation with Pandas Data Frames (1)
	Data Manipulation with Pandas Data Frames (2
)
	Data Manipulation with Pandas Data Frames (3
)
	Pandas Data Frames and CSV Files
	Managing Columns in Data Frames
		Switching Columns
		Appending Columns
		Deleting Columns
		Inserting Columns
		Scaling Numeric Columns
	Managing Rows in Pandas
		Selecting a Range of Rows in Pandas
		Finding Duplicate Rows in Pandas
		Inserting New Rows in Pandas
	Handling Missing Data in Pandas
		Multiple Types of Missing Values
		Test for Numeric Values in a Column
		Replacing NaN Values in Pandas
	Sorting Data Frames in Pandas
	Working with groupby() in Pandas
	Working with apply() and mapapply() in Pandas
	Handling Outliers in Pandas
	Pandas Data Frames and Scatterplots
	Pandas Data Frames and Simple Statistics
	Aggregate Operations in Pandas Data Frames
	Aggregate Operations with the titanic.csv Dataset
	Save Data Frames as CSV Files and Zip Files
	Pandas Data Frames and Excel Spreadsheets
	Working with JSON-based Data
		Python Dictionary and JSON
		Python, Pandas, and JSON
	Pandas and Regular Expressions (Optional)
	Useful One-Line Commands in Pandas
	What is Method Chaining?
		Pandas and Method Chaining
	Pandas Profiling
	What is Texthero?
	Summary
Chapter 3 NLP Concepts (I)
	The Origin of Languages
		Language Fluency
		Major Language Groups
		Peak Usage of Some Languages
		Languages and Regional Accents
		Languages and Slang
		Languages and Dialects
	The Complexity of Natural Languages
		Word Order in Sentences
		What about Verbs?
		Auxiliary Verbs
		What are Case Endings?
		Languages and Gender
		Singular and Plural Forms of Nouns
		Change in Spelling of Words
	Japanese Grammar
		Japanese Postpositions (Particles)
		Ambiguity in Japanese Sentences
		Japanese Nominalization
		Google Translate and Japanese
		Japanese and Korean
		Vowel-Optional Languages and Word Direction
		Mutating Consonant Spelling
		Expressing Negative Opinions
	Phonetic Languages
		Phonemes and Morphemes
		English Words of Greek and Latin Origin
	Multiple Ways to Pronounce Consonants
		The Letter “j” in Various Languages
		“Hard” versus “Soft” Consonant Sounds
		“Ess,” “zee,” and “sh” Sounds
		Three Consecutive Consonants
		Diphthongs and Triphthongs in English
		Semi-Vowels in English
		Challenging English Sounds
		English in Canada, UK, Australia, and the United States
	English Pronouns and Prepositions
	What is NLP?
		The Evolution of NLP
	A Wide-Angle View of NLP
		NLP Applications and Use Cases
		NLU and NLG
		What is Text Classification?
	Information Extraction and Retrieval
	Word Sense Disambiguation
	NLP Techniques in ML
		NLP Steps for Training a Model
	Text Normalization and Tokenization
		Word Tokenization in Japanese
		Text Tokenization with Unix Commands
	Handling Stop Words
	What is Stemming?
		Singular versus Plural Word Endings
		Common Stemmers
		Stemmers and Word Prefixes
		Over Stemming and Under Stemming
	What is Lemmatization?
		Stemming/Lemmatization Caveats
		Limitations of Stemming and Lemmatization
	Working with Text: POS
		POS Tagging
		POS Tagging Techniques
	Working with Text: NER
		Abbreviations and Acronyms
		NER Techniques
	What is Topic Modeling?
	Keyword Extraction, Sentiment Analysis, and Text Summarization
	Summary
Chapter 4 NLP Concepts (II)
	What is Word Relevance?
	What is Text Similarity
?
	Senence Similarity
		Sentence Encoders
	Working with Documents
		Working with Documents
		Document Similarity (doc2vec)
	Techniques for Text Similarity
		Similarity Queries
	What is Text Encoding?
	Text Encoding Techniques
		Document Vectorization
		One-Hot Encoding (OHE)
		Index-Based Encoding
		Additional Encoders
	The BoW Algorithm
	What are n-grams?
		Calculating Probabilities with N-grams
	Calculating tf, idf, and tf-idf
		What is Term Frequency (TF)?
		What is Inverse Document Frequency (IDF)?
		What is tf-idf?
		Limitations of tf-idf
		Pointwise Mutual Information (PMI)
	The Context of Words in a Document
		What is Semantic Context?
		Textual Entailment
		Discrete, Distributed, and Contextual Word Representations
	What is Cosine Similarity?
	Text Vectorization (aka Word Embeddings)
	Overview of Word Embeddings and Algorithms
		Word Embeddings
		Word Embedding Algorithms
	What is Word2vec?
		The Intuition for Word2vec
		The Word2vec Architecture
		Limitations of Word2vec
	The CBoW Architecture
	What are Skip-grams?
		Skip-gram Example
		The Skip-gram Architecture
		Neural Network Reduction
	What is GloVe?
	Working with GloVe
	What is FastText?
	Comparison of Word Embeddings
	What is Topic Modeling?
		Topic Modeling Algorithms
		LDA and Topic Modeling
		Text Classification versus Topic Modeling
	Language Models and NLP
		How to Create a Language Model
	Vector Space Models
		Term-Document Matrix
		Tradeoffs of the VSM
	NLP and Text Mining
		Text Extraction Preprocessing and N-Grams
	Relation Extraction and Information Extraction
	What is a BLEU Score?
		ROUGE Score: An Alternative to BLEU
	Summary
Chapter 5 Algorithms and Toolkits (I)
	Cleaning Data with Regular Expressions
	Handling Contracted Words
	Python Code Samples of BoW
	One-Hot Encoding Examples
	Sklearn and Word Embedding Examples
	What is BeautifulSoup?
	Web Scraping with Pure Regular Expressions
	What is Scrapy?
	What is SpaCy?
	SpaCy and Stop Words
	SpaCy and Tokenization
	SpaCy and Lemmatization
	SpaCy and NER
	SpaCy Pipelines
	SpaCy and Word Vectors
	The scispaCy Library (Optional)
	Summary
Chapter 6 Algorithms and Toolkits (II)
	What is NLTK?
	NLTK and BoW
	NLTK and Stemmers
	NLTK and Lemmatization
	NLTK and Stop Words
	What is Wordnet?
		Synonyms and Antonyms
	NLTK, lxml, and XPath
	NLTK and n-grams
	NLTK and POS (1)
	NLTK and POS (2
)
	NLTK and Tokenizers
	NLTK and Context-Free Grammars (Optional)
	What is Gensim?
		Gensim and tf-idf Example
		Saving a Word2vec Model in Genism
	An Example of Topic Modeling
	A Brief Comparison of Popular Python-Based NLP Libraries
	Miscellaneous Libraries
	Summary
Chapter 7 Introduction to Machine Learning
	What is Machine Learning?
		Learning Style of Machine Learning Algorithms
	Types of Machine Learning Algorithms
		Machine Learning Tasks
	Preparing a Dataset and Training a Model
	Feature Engineering, Selection, and Extraction
		Feature Engineering
		Feature Selection
		Feature Extraction
		Model Selection
	Working with Datasets
		Training Data versus Test Data
		What is Cross-Validation?
	Overfitting versus Underfitting
		What is Regularization?
		ML and Feature Scaling
	Data Normalization Techniques
	Metrics in Machine Learning
		R-Squared and its Limitations
		Confusion Matrix
		Precision, Recall, and Specificity
		The ROC Curve and AUC
		Metrics for Model Evaluation and Selection
	What is Linear Regression?
		Linear Regression versus Curve-Fitting
		When are Solutions Exact Values?
		What is Multivariate Analysis?
	Other Types of Regression
	Working with Lines in the Plane (Optional)
	Scatter Plots with NumPy and Matplotlib (1)
		Why the “Perturbation Technique” is Useful
	Scatter Plots with NumPy and Matplotlib (2)
	A Quadratic Scatterplot with NumPy and Matplotlib
	The Mean Squared Error (MSE) Formula
		A List of Error Types
		Nonlinear Least Squares
	Calculating the MSE Manually
	Approximating Linear Data with np.linspace()
	What are Ensemble Methods?
	Four Types of Ensemble Methods
		Bagging
		Boosting
		Stacked Models and Blending Models
		What is Bootstrapping?
	Common Boosting Algorithms
	Hyperparameter Optimization
		Grid Search
		Randomized Search
		Bayesian Optimization
	AutoML, AutoML-Zero, and AutoNLP
	Miscellaneous Topics
		What is Causality?
		What is Explainability?
		What is Interpretability?
	Summary
Chapter 8 
Classifiers in Machine Learning
	What is Classification?
		What are Classifiers?
		Common Classifiers
		Binary versus Multiclass Classification
		Multilabel Classification
	What are Linear Classifiers?
	What is kNN?
		How to Handle a Tie in kNN
		SMOTE and kNN
		kNN for Data Imputation
	What are Decision Trees?
		Trade-offs with Decision Trees
		Decision Tree Algorithms
	Decision Tree Code Samples
	Decision Trees, Gini Impurity, and Entropy
	What are Random Forests?
	What are Support Vector Machines?
		Trade-offs of SVMs
	What is a Bayesian Classifier?
		Types of Naïve Bayes Classifiers
	Training Classifiers
	Evaluating Classifiers
	Trade-offs for ML Algorithms
	What are Activation Functions?
		Why Do we Need Activation Functions?
		How Do Activation Functions Work?
	Common Activation Functions
		Activation Functions in Python
		Keras Activation Functions
	The ReLU and ELU Activation Functions
		The Advantages and Disadvantages of ReLU
		Elu
	Sigmoid, Softmax, and Hardmax Similarities
		Softmax
		Softplus
		Tanh
	Sigmoid, Softmax, and HardMax Differences
	Hyperparameters for Neural Networks
		The Loss Function Hyperparameter
		The Optimizer Hyperparameter
		The Learning Rate Hyperparameter
		The Dropout Rate Hyperparameter
		What is Backward Error Propagation?
	What is Logistic Regression?
		Setting a Threshold Value
		Logistic Regression: Important Assumptions
		Linearly Separable Data
	Keras, Logistic Regression, and Iris Dataset
	Sklearn and Linear Regression
	SciPy and Linear Regression
	Keras and Linear Regression
	Summary
Chapter 9 
NLP Applications
	What is Text Summarization?
		Extractive Text Summarization
		Abstractive Text Summarization
	Text Summarization with gensim and SpaCy
	What are Recommender Systems?
		Movie Recommender Systems
		Factoring the Rating Matrix R
	Content-Based Recommendation Systems
		Analyzing only the Description of the Content
		Building User Profiles and Item Profiles
	Collaborative Filtering Algorithm
		User–User Collaborative Filtering
		Item–Item Collaborative Filtering
		Recommender System with Surprise
	Recommender Systems and Reinforcement Learning (Optional)
		Basic Reinforcement Learning in Five Minutes
		What is RecSim?
	What is Sentiment Analysis?
		Useful Tools for Sentiment Analysis
		Aspect-Based Sentiment Analysis
		Deep Learning and 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
	Working with COVID-19
	What are Chatbots?
		Open Domain Chatbots
		Chatbot Types
		Logic Flow of Chatbots
		Chatbot Abuses
		Useful Links
	Summary
Chapter 10 
NLP and TF2/Keras
	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
	Summary
Chapter 11 
Transformer, Bert, and Gpt
	What is Attention?
		Types of Word Embeddings
		Types of Attention and Algorithms
	An Overview of the Transformer Architecture
		The Transformers Library from HuggingFace
		Transformer and NER Tasks
		Transformer and QnA Tasks
		Transformer and Sentiment Analysis Tasks
		Transformer and Mask Filling Tasks
	What is T5?
	What is BERT?
		BERT Features
		How is BERT Trained?
		How BERT Differs from Earlier NLP Techniques
	The Inner Workings of BERT
		What is MLM?
		What is NSP?
		Special Tokens
		BERT Encoding: Sequence of Steps
	Subword Tokenization
	Sentence Similarity in BERT
	Word Context in BERT
	Generating BERT Tokens (1)
	Generating BERT Tokens (2
)
	The BERT Family
		Surpassing Human Accuracy: deBERTa
		What is Google Smith?
	Introduction to GPT
		Installing the Transformers Package
	Working with GPT-2
	What is GPT-3?
		What is the Goal?
		GPT-3 Task Strengths and Mistakes
		GPT-3 Architecture
		GPT versus BERT
		Zero-Shot, One-Shot, and Few Shot Learners
		GPT Task Performance
	The Switch Transformer: One Trillion Parameters
	Looking Ahead
	Summary
Appendix A Data and Statistics
	What are Datasets?
		Data Preprocessing
		Data Types
	Preparing Datasets
		Continuous versus Discrete Data
		“Binning” Continuous Data
		Scaling Numeric Data via Normalization
		Scaling Numeric Data via Standardization
		What to Look for in Categorical Data
		Mapping Categorical Data to Numeric Values
		Working with Dates
		Working with Currency
	Missing Data, Anomalies, and Outliers
		Anomalies and Outliers
		Outlier Detection
		Missing Data: MCAR, MAR, and MNAR
		What is Data Drift?
	What is Imbalanced Classification?
		Undersampling and Oversampling
		Limitations of Resampling
	What is SMOTE?
		SMOTE Extensions
	Analyzing Classifiers
		What is LIME?
		What is ANOVA?
	What is a Probability?
		Calculating the Expected Value
	Random Variables
		Discrete versus Continuous Random Variables
		Well-Known Probability Distributions
	Fundamental Concepts in Statistics
		The Mean
		The Median
		The Mode
		The Variance and Standard Deviation
		Population, Sample, and Population Variance
		Chebyshev’s Inequality
		What is p-Value?
	The Moments of a Function (Optional)
		Skewness
		Kurtosis
	Data and Statistics
		The Central Limit Theorem
		Correlation versus Causation
		Statistical Inferences
	The Bias-Variance Trade-off
		Types of Bias in Data
	Gini Impurity, Entropy, and Perplexity
		What is Gini Impurity?
		What is Entropy?
		Calculating Gini Impurity and Entropy Values
		Multidimensional Gini Index
		What is Perplexity?
	Cross-Entropy and KL Divergence
		What is Cross Entropy?
		What is KL Divergence?
		What’s their Purpose?
	Covariance and Correlation Matrices
		Covariance Matrix
		Covariance Matrix: An Example
		Correlation Matrix
		Eigenvalues and Eigenvectors
		Calculating Eigenvectors: A Simple Example
		Gauss Jordan Elimination (Optional)
	Principal Component Analysis (PCA)
		The New Matrix of Eigenvectors
	Dimensionality Reduction
	Dimensionality Reduction Techniques
		The Curse of Dimensionality
		What are Manifolds (Optional)?
		Singular Value Decomposition (SVD)
		Locally Linear Embedding (LLE)
		UMAP
		t-SNE (“tee-snee”)
		PHATE
	Linear Versus Nonlinear Reduction Techniques
	Types of Distance Metrics
	Other Well-Known Distance Metrics
		Pearson Correlation Coefficient
		Jaccard Index (or Similarity)
		Local Sensitivity Hashing (Optional)
	What is Sklearn?
		Sklearn, Pandas, and the IRIS Dataset
		Sklearn and Outlier Detection
	What is Bayesian Inference?
		Bayes Theorem
		Some Bayesian Terminology
		What is MAP?
		Why Use Bayes Theorem?
	What are Vector Spaces?
	Summary
Appendix B Introduction to Python
	Tools for Python
		easy_install and pip
		virtualenv
		IPython
	Python Installation
	Setting the PATH Environment Variable (Windows Only)
	Launching Python on Your Machine
		The Python Interactive Interpreter
	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 Other Bases
		The chr () Function
		The round() Function in Python
		Formatting Numbers in Python
	Working with Fractions
	Unicode and UTF-8
	Working with Unicode
	Working with Strings
		Comparing Strings
		Formatting Strings in Python
	Uninitialized Variables and the Value None in Python
	Slicing and Splicing Strings
		Testing for Digits and Alphabetic Characters
	Search and Replace a String in Other Strings
	Remove Leading and Trailing Characters
	Printing Text without NewLine Characters
	Text Alignment
	Working with Dates
		Converting Strings to Dates
	Exception Handling in Python
	Handling User Input
	Python and Emojis (Optional)
	Command-Line Arguments
	Summary
Appendix C Introduction to Regular Expressions
	What are Regular Expressions?
	Metacharacters in Python
	Character Sets in Python
		Working with “^” and “\”
	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
		Finding Capitalized Words in a String
	Additional Matching Function for Regular Expressions
	Grouping with Character Classes in Regular Expressions
	Using Character Classes in Regular Expressions
		Matching Strings with Multiple Consecutive Digits
		Reversing Words in Strings
	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
	Summary
	Exercises
Appendix D Introduction to Keras
	What is Keras?
		Working with Keras Namespaces in TF 2
		Working with the tf.keras.layers Namespace
		Working with the tf.keras.activations Namespace
		Working with the keras.tf.datasets Namespace
		Working with the tf.keras.experimental Namespace
		Working with Other tf.keras Namespaces
		TF 2 Keras versus “Standalone” 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
	Summary
Appendix E 
Introduction to TensorFlow 2
	What is TF 2?
		TF 2 Use Cases
		TF 2 Architecture: The Short Version
		TF 2 Installation
		TF 2 and the Python REPL
	Other TF 2-Based Toolkits
	TF 2 Eager Execution
	TF 2 Tensors, Data Types, and Primitive Types
		TF 2 Data Types
		TF 2 Primitive Types
	Constants in TF 2
	Variables in TF 2
	The tf.rank() API
	The tf.shape() API
	Variables in TF 2 (Revisited)
		TF 2 Variables versus Tensors
	What is @tf.function in TF 2?
		How Does @tf.function Work?
		A Caveat about @tf.function in TF 2
		The tf.print() Function and Standard Error
	Working with @tf.function in TF 2
		An Example without @tf.function
		An Example with @tf.function
		Overloading Functions with @tf.function
		What is AutoGraph 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
		Conflicting Types in TF 2
	Differentiation and tf.GradientTape in TF 2
	Examples of tf.GradientTape
		Using Nested Loops with tf.GradientTape
		Other Tensors with tf.GradientTape
		A Persistent Gradient Tape
	What is Trax?
	Google Colaboratory
	Other Cloud Platforms
		GCP SDK
	TF2 and tf.data.Dataset
	The TF 2 tf.data.Dataset
		Creating a Pipeline
		A Simple TF 2 tf.data.Dataset
	What are Lambda Expressions?
	Working with Generators in TF 2
	Summary
Appendix F Data Visualization
	What is Data Visualization?
		Types of 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
	Plot a Best-Fitting Line 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
	Introduction to Sklearn (scikit-learn)
	The Digits Dataset in Sklearn
	The Iris Dataset in Sklearn
		Sklearn, Pandas, and the Iris Dataset
	The Iris Dataset in Sklearn (Optional)
	The faces Dataset in Sklearn (Optional)
	Working with Seaborn
		Features of 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
	Summary
Index