Data Without Labels: Practical unsupervised machine learning

Data Without Labels....1 Praise for Data Without Labels....3 brief contents....8 contents....9 foreword....16 preface....18 acknowledgments....20 about this book....22 Who should read this book....22 How this book is organized: A road map....23 About the code....23 liveBook discussion forum....23 about the author....25 about the cover illustration....26 Part 1 Basics....27 1 Introduction to machine learning....29 1.1 Technical toolkit....30 1.2 Data, data types, data management, and quality....31 1.2.1 What is data?....31 1.2.2 Various types of data....32 1.2.3 Data quality....35 1.2.4 Data engineering and management....37 1.3 Data analysis, ML, AI, and business intelligence....38 1.4 Nuts and bolts of ML....40 1.5 Types of ML algorithms....43 1.5.1 Supervised learning....44 1.5.2 Unsupervised algorithms....50 1.5.3 Semisupervised algorithms....54 1.5.4 Reinforcement learning....54 1.6 Concluding thoughts....55 Summary....56 2 Clustering techniques....58 2.1 Technical toolkit....59 2.2 Clustering....60 2.3 Centroid-based clustering....63 2.3.1 K-means clustering....65 2.3.2 Measuring the accuracy of clustering....68 2.3.3 Finding the optimum value of k....69 2.3.4 Pros and cons of k-means clustering....70 2.3.5 K-means clustering implementation using Python....72 2.4 Connectivity-based clustering....76 2.4.1 Types of hierarchical clustering....78 2.4.2 Linkage criterion for distance measurement....79 2.4.3 Optimal number of clusters....80 2.4.4 Pros and cons of hierarchical clustering....82 2.4.5 Hierarchical clustering case study using Python....83 2.5 Density-based clustering....86 2.5.1 Neighborhood and density....86 2.5.2 DBSCAN clustering....88 2.6 Case study using clustering....93 2.6.1 Business context....94 2.6.2 Dataset for the analysis....95 2.6.3 Suggested solutions....96 2.6.4 Solution for the problem....96 2.7 Common challenges faced in clustering....98 2.8 Concluding thoughts....100 2.9 Practical next steps and suggested readings....100 Summary....101 3 Dimensionality reduction....103 3.1 Technical toolkit....104 3.2 The curse of dimensionality....104 3.3 Dimension reduction methods....108 3.3.1 Mathematical foundation....108 3.4 Manual methods of dimensionality reduction....108 3.4.1 Manual feature selection....109 3.4.2 Correlation coefficient....110 3.4.3 Algorithm-based methods for reducing dimensions....111 3.5 Principal component analysis....111 3.5.1 Eigenvalue decomposition....116 3.5.2 Python solution using PCA....117 3.6 Singular value decomposition....123 3.6.1 Python solution using SVD....124 3.7 Pros and cons of dimensionality reduction....127 3.8 Case study for dimension reduction....129 3.9 Concluding thoughts....132 3.10 Practical next steps and suggested readings....132 Summary....133 Part 2 Intermediate level....135 4 Association rules....137 4.1 Technical toolkit....138 4.2 Association rule overview....138 4.3 The building blocks of association rules....140 4.3.1 Support, confidence, lift, and conviction....141 4.4 Apriori algorithm....145 4.4.1 Python implementation....147 4.4.2 Challenges with the Apriori algorithm....151 4.5 Equivalence class clustering and bottom-up lattice traversal....152 4.5.1 Python implementation....155 4.6 F-P algorithm....156 4.7 Sequence rule mining....163 4.7.1 Sequential Pattern Discovery Using Equivalence....164 4.8 Case study for association rules....168 4.9 Concluding thoughts....171 4.10 Practical next steps and suggested readings....173 Summary....173 5 Clustering....175 5.1 Technical toolkit....176 5.2 Clustering: A brief recap....176 5.3 Spectral clustering....177 5.3.1 Building blocks of spectral clustering....179 5.3.2 The process of spectral clustering....182 5.4 Python implementation of spectral clustering....184 5.5 Fuzzy clustering....186 5.5.1 Types of fuzzy clustering....187 5.5.2 Python implementation of FCM....190 5.6 Gaussian mixture model....193 5.6.1 EM technique....195 5.6.2 Python implementation of GMM....197 5.7 Concluding thoughts....200 5.8 Practical next steps and suggested readings....200 Summary....201 6 Dimensionality reduction....202 6.1 Technical toolkit....203 6.2 Multidimensional scaling....203 6.2.1 Classic MDS....205 6.2.2 Nonmetric MDS....206 6.3 Python implementation of MDS....210 6.4 t-distributed stochastic neighbor embedding....215 6.4.1 Cauchy distribution....217 6.4.2 Python implementation of t-SNE....219 6.5 Uniform manifold approximation projection....222 6.5.1 Working with UMAP....223 6.5.2 Using UMAP....223 6.5.3 Key points of UMAP....224 6.6 Case study....224 6.7 Concluding thoughts....226 6.8 Practical next steps and suggested readings....226 Summary....227 7 Unsupervised learning for text data....228 7.1 Technical toolkit....229 7.2 Text data is everywhere....229 7.3 Use cases of text data....230 7.4 Challenges with text data....231 7.5 Preprocessing the text data....233 7.6 Data cleaning....233 7.7 Extracting features from the text dataset....235 7.8 Tokenization....236 7.9 BOW approach....237 7.10 Term frequency and inverse document frequency....239 7.11 Language models....240 7.12 Text cleaning using Python....242 7.13 Word embeddings....245 7.14 Word2Vec and GloVe....247 7.15 Sentiment analysis case study with Python implementation....248 7.16 Text clustering using Python....254 7.17 GenAI for text data....256 7.18 Concluding thoughts....256 7.19 Practical next steps and suggested readings....257 Summary....258 Part 3 Advanced concepts....259 8 Deep learning: The foundational concepts....261 8.1 Technical toolkit....262 8.1.1 Deep learning: What is it? What does it do?....262 8.2 Building blocks of a neural network....264 8.2.1 Neural networks for solutions....264 8.2.2 Artificial neurons and perceptrons....265 8.2.3 Different layers in a network....267 8.2.4 Activation functions....269 8.2.5 Hyperparameters....271 8.2.6 Optimization functions....272 8.3 How does deep learning work in a supervised manner?....274 8.3.1 Supervised learning algorithms....274 8.3.2 Step 1: Feed-forward propagation....274 8.3.3 Step 2: Adding the loss function....275 8.3.4 Step 3: Calculating the error....276 8.4 Backpropagation....276 8.4.1 The mathematics behind backpropagation....277 8.4.2 Step 4: Optimization....279 8.5 How deep learning works in an unsupervised manner....279 8.6 Convolutional neural networks....280 8.6.1 Key concepts of CNN....280 8.6.2 Use of CNN....282 8.7 Recurrent neural networks....282 8.7.1 Key concepts of RNN....282 8.8 Boltzmann learning rule....284 8.8.1 Concepts of the Boltzmann learning rule....284 8.8.2 Key points....285 8.9 Deep belief networks....285 8.9.1 Key points of DBN....285 8.10 Popular deep learning libraries....287 8.10.1 Python code for Keras and TF....288 8.11 Concluding thoughts....289 8.12 Practical next steps and suggested readings....290 Summary....291 9 Autoencoders....293 9.1 Technical toolkit....293 9.2 Feature learning....294 9.3 Introducing autoencoders....294 9.4 Components of autoencoders....295 9.5 Training of autoencoders....296 9.6 Application of autoencoders....297 9.7 Types of autoencoders....297 9.8 Python implementation of autoencoders....301 9.9 Concluding thoughts....303 9.10 Practical next steps and suggested readings....303 Summary....304 10 Generative adversarial networks, generative AI, and ChatGPT....305 10.1 AI: A transformation....305 10.2 GenAI and its significance....306 10.3 Discriminative models and GenAI....308 10.4 Generative adversarial networks....309 10.4.1 The generator network....309 10.4.2 The discriminator network....310 10.4.3 Adversarial training....311 10.4.4 Variants and applications of GANs....312 10.4.5 BERT, GPT-3, and others....312 10.5 ChatGPT and its details....313 10.5.1 Key features of ChatGPT....313 10.5.2 Applications of ChatGPT....313 10.6 Integration of GenAI....314 10.7 Concluding thoughts....315 10.8 Practical next steps and suggested readings....316 Summary....316 11 End-to-end model deployment....317 11.1 The machine learning modeling process....318 11.2 Business problem definition....318 11.3 Data discovery and feasibility analysis....320 11.4 Data cleaning and prepreparation....321 11.5 Duplicate values in the data....321 11.6 Categorical variables....322 11.7 Missing values in dataset....323 11.8 Outliers present in the data....325 11.9 Exploratory data analysis....325 11.10 Model development and business approval....326 11.11 Model deployment....326 11.12 Purpose of model deployment....326 11.13 Types of model deployment....327 11.14 Considerations while deploying the model....328 11.15 Documentation....329 11.16 Model maintenance and refresh....329 11.17 Concluding thoughts....330 11.18 Practical next steps and suggested readings....331 Summary....331 appendix A Mathematical foundations....333 A.1 List of clustering algorithms....333 A.1.1 Partitioning-based algorithms....333 A.1.2 Hierarchical clustering....333 A.1.3 Density-based algorithms....333 A.1.4 Grid-based algorithms....334 A.1.5 Model-based algorithms....334 A.1.6 Spectral clustering....334 A.1.7 Graph-based clustering....334 A.1.8 Subspace and high-dimensional clustering....335 A.1.9 Fuzzy and soft clustering....335 A.1.10 Constraint-based clustering....335 A.1.11 Evolutionary and genetic clustering....335 A.1.12 Neural network-based clustering....336 A.1.13 Other algorithms....336 A.2 What is a centroid?....336 A.3 L1 vs. L2 norm....336 A.4 Different scaling techniques used in the industry....336 A.5 Time complexity O(n)....337 A.6 How to install packages in Python....338 A.7 Correlation....338 A.7.1 Correlation coefficient....339 A.7.2 Uses of correlation....339 A.7.3 Important considerations....339 A.8 Time-series analysis....340 A.9 Mathematical foundation for data representation....340 A.9.1 Scalar and vector....341 A.9.2 Standard deviation and variance....341 A.9.3 Covariance and correlation....342 A.9.4 Matrix decomposition, eigenvectors, and eigenvalues....343 A.9.5 Special matrices....344 A.10 Hyperparameters vs. parameters....344 index....345 A....345 B....345 C....346 D....346 E....347 F....347 G....348 H....348 I....348 J....348 K....349 L....349 M....349 N....350 O....350 P....350 Q....351 R....351 S....351 T....351 U....352 V....352 W....352 X....352 Y....352 Z....352 Data Without Labels - back....354