Thinking Data Science: A Data Science Practitioner’s Guide

Preface
Contents
1: Data Science Process
	Traditional Model Building
	Modern Approach for Model Building
	AI on Image Datasets
	Model Development on Text Datasets
	Model Building on High-Frequency Datasets
	Data Science Process
		Data Preparation
		Numeric Data Processing
		Text Processing
		Preprocessing Text Data
		Exploratory Data Analysis
		Features Engineering
		Deciding on Model Type
		Model Training
		Algorithm Selection
	AutoML
	Hyper-Parameter Tuning
	Model Building Using ANN
	Models Based on Transfer Learning
	Summary
2: Dimensionality Reduction
	In a Nutshell
	Why Reduce Dimensionality?
	Dimensionality Reduction Techniques
		Project Dataset
		Columns with Missing Values
		Filtering Columns Based on Variance
		Filtering Highly Correlated Columns
		Random Forest
		Backward Elimination
		Forward Features Selection
		Factor Analysis
		Principal Component Analysis
		PCA on Huge Multi-columnar Dataset
			About the Dataset
			Loading Dataset
			Model Building
			PCA for Visualization
			PCA for Model Building
		Independent Component Analysis
		Isometric Mapping
		t-Distributed Stochastic Neighbor Embedding (t-SNE)
		UMAP
		Singular Value Decomposition
		Linear Discriminant Analysis (LDA)
	Summary
Part I: Classical Algorithms: Overview
3: Regression Analysis
	In a Nutshell
	When to Use?
	Regression Types
		Linear Regression
			Assumptions
		Polynomial Regression
		Ridge Regression
		Lasso Regression
		ElasticNet Regression
		Linear Regression Implementations
			Linear Regression
			Ridge Regression
			Lasso Regression
	Bayesian Linear Regression
		BLR Implementation
		BLR Project
	Logistic Regression
		Logistic Regression Implementation
	Guidelines for Model Selection
	What´s Next?
	Summary
4: Decision Tree
	In a Nutshell
	Wide Range of Applications
	Decision Tree Workings
		Tree Traversal
	Tree Construction
		Entropy
		Information Gain
		Gini Index
	Constructing Tree
		Tree Construction Algorithm
		Tree Traversal Algorithm
	Implementation
	Project (Regression)
		Loading Dataset
		Preparing Datasets
		Model Building
		Evaluating Performance
		Tree Visualization
		Feature Importance
	Project (Classifier)
	Summary
5: Ensemble: Bagging and Boosting
	What is Bagging and Boosting?
		Bagging
		Boosting
	Random Forest
		In a Nutshell
		What Is Random Forest?
		Random Forest Algorithm
		Advantages
		Applications
		Implementation
		Random Forest Project
	ExtraTrees
		Bagging Ensemble Project
		ExtraTreesRegressor
		ExtraTreesClassifier
	Bagging
		BaggingRegressor
		BaggingClassifier
	AdaBoost
		How Does It Work?
		Implementation
		AdaBoostRegressor
		AdaBoost Classifier
		Advantages/Disadvantages
	Gradient Boosting
		Loss Function
		Requirements for Gradient Boosting
		Implementation
		GradientBoostingRegressor
		AdaBoostClassifier
		Pros and Cons
	XGBoost
		Implementation
		XGBRegressor
		XGBClassifier
	CatBoost
		Implementation
		CatBoostRegressor
		CatBoostClassifier
	LightGBM
		Implementation
		The LGBMRegressor
		The LGBMClassifier
	Performance Summary
	Summary
6: K-Nearest Neighbors
	In a Nutshell
	K-Nearest Neighbors
	KNN Algorithm
	KNN Working
	Effect of K
	Advantages
	Disadvantages of KNN
		Implementation
	Project
		Loading Dataset
		Determining K Optimal
		Model Training
		Model Testing
	When to Use?
	Summary
7: Naive Bayes
	In a Nutshell
	When to Use?
	Naive Bayes Theorem
		Applying the Theorem
		Advantages
		Disadvantages
		Improving Performance
	Naive Bayes Types
		Multinomial Naive Bayes
		Bernoulli Naive Bayes
		Gaussian Naive Bayes
		Complement Naive Bayes
		Categorical Naive Bayes
	Model Fitting for Huge Datasets
	Project
		Preparing Dataset
		Data Visualization
		Model Building
		Inferring on Unseen Data
	Summary
8: Support Vector Machines
	In a Nutshell
	SVM Working
	Hyperplane Types
	Kernel Effects
		Linear Kernel
		Polynomial Kernel
		Radial Basis Function
		Sigmoid
		Guidelines on Kernel Selection
	Parameter Tuning
		The C Parameter
		The Degree Parameter
		The Gamma Parameter
		The decision_function_shape Parameter
	Project
	Advantages and Disadvantages
	Summary
Part II: Clustering: Overview
9: Centroid-Based Clustering
	The K-Means Algorithm
		In a Nutshell
		How Does It Work?
		K-Means Algorithm
		Objective Function
			The Process Workflow
		Selecting Optimal Clusters
			Elbow Method
			Average Silhouette Method
			The Gap Statistic Method
		Limitations of K-Means Clustering
		Applications
		Implementation
		Project
	The K-Medoids Algorithm
		In a Nutshell
		Why K-Medoids?
		Algorithm
		Merits and Demerits
		Implementation
	Summary
10: Connectivity-Based Clustering
	Agglomerative Clustering
		In a Nutshell
		The Working
			Single Linkage
			Complete Linkage
			Average Linkage
		Advantages and Disadvantages
		Applications
		Implementation
	Project
	Divisive Clustering
		In a Nutshell
		The Working
		Implementation Challenges
	Summary
11: Gaussian Mixture Model
	In a Nutshell
	Gaussian Distribution
	Probability Distribution
	Selecting Number of Clusters
	Implementation
	Project
	Determining Optimal Number of Clusters
	Summary
12: Density-Based Clustering
	DBSCAN
		In a Nutshell
		Why DBSCAN?
		Preliminaries
		Algorithm Working
		Advantages and Disadvantages
		Implementation
		Project
	OPTICS
		In a Nutshell
		Core Distance
		Reachability Distance
		Implementation
		Project
	Mean Shift Clustering
		In a Nutshell
		Algorithm Working
			Bandwidth Selection
			Strengths
			Weaknesses
			Applications
		Implementation
		Project
	Summary
13: BIRCH
	In a Nutshell
	Why BIRCH?
		Clustering Feature
		CF Tree
		BIRCH Algorithm
		Implementation
	Project
	Summary
14: CLARANS
	In a Nutshell
	CLARA Algorithm
	CLARANS Algorithm
	Advantages
	Project
	Summary
15: Affinity Propagation Clustering
	In a Nutshell
	Algorithm Working
		Responsibility Matrix Updates
		Availability Matrix Updates
		Updating Scores
		Few Remarks
	Implementation
	Project
	Summary
16: STING & CLIQUE
	STING: A Grid-Based Clustering Algorithm
		In a Nutshell
		How Does It Work?
		Advantages and Disadvantages
		Applications
	CLIQUE: Density- and Grid-Based Subspace Clustering Algorithm
		In a Nutshell
		How Does It Work?
		Pros/Cons
		Implementation
		Project
	Summary
Part III: ANN: Overview
17: Artificial Neural Networks
	AI Evolution
	Artificial Neural Networks
		Perceptron
		What Is ANN?
		Network Training
		ANN Architectures
		What Is DNN?
		Network Architectures
		What Are Pre-trained Models?
	Important Terms to Know
		Activation Functions
		Back Propagation
		Vanishing and Exploding Gradients
		Optimization Functions
		Types of Optimizers
		Loss Functions
			Regression Loss Functions
			Classification Loss Functions
	Types of Network Architectures
	Convolutional Neural Network
		Convolutional Layer
		Pooling Layer
		Fully Connected Layer
		CNN Applications
	Generative Adversarial Network
		Model Architecture
		The Generator
		The Discriminator
		How Does GAN Work?
		How Data Scientists Use GAN?
	Recurrent Neural Networks (RNN)
	Long Short-Term Memory (LSTM)
		Forget Gate
		Input Gate
		Update Gate
		Output Gate
		LSTM Applications
		Transfer Learning
		Pre-trained Models for Text
			Word2Vec
			Glove
			Transformer
			BERT
			GPT
		Pre-trained Models for Image Data
	Advantages/Disadvantages
	Summary
18: ANN-Based Applications
	Developing NLP Applications
		Dataset
		Text Preprocessing
		Using BERT
			Creating Training/Testing Datasets
			Setting Up BERT
			Model Building
			Model Training
			Model Evaluation
		Using Embeddings
			N-gram Analysis
			Tokenizing
			Remove Stop Words
			Model Building
				Using Own Embeddings: Model 0
				Embedding Weight Matrix
				Glove: Model 1
				Glove: Model 2
				Glove: Model 3
		Final Thoughts
	Developing Image-Based Applications
		Data Preparation
		Modeling
			CNN-Based Network
			VGG16
			ResNet50
			MobileNet
			DenseNet121
		Summarizing Observations
		Modeling on High-Resolution Images
		Inferring Web Images
	Summary
19: Automated Tools
	In a Nutshell
	Classical AI
		Auto-sklearn
		Auto-sklearn for Classification on Synthetic Dataset
		Auto-sklearn for Classification on Real Dataset
		Auto-sklearn for Regression
		Auto-sklearn Architecture
		Auto-sklearn Features
		What´s Next?
	ANN/DNN
		AutoKeras for Classification
		AutoKeras for Regression
		AutoKeras Image Classifier
	More AutoML Frameworks
		PyCaret
		MLBox
		TPOT
		H2O.ai
		DataRobot
		DataBricks
		BlobCity AutoAI
	Summary
20: Data Scientist´s Ultimate Workflow
	Consolidated Overview
	Workflow-0: Quick Solution
	Workflow-1: Technology Selection
	Workflow-2: Data Preprocessing
	Workflow-3: EDA
	Workflow-4: Features Engineering
	Workflow-5: Type of Task
	Workflow-6: Preparing Datasets
	Workflow-7: Algorithm Selections
	Workflow-8: AutoML
	Workflow-9: Hyper-parameter Tuning
	Workflow-10: ANN Model Building
	Workflow-11: Clustering
	Summary