{"product_id":"data-science-programming-allinone-for-dummies-9781119626114","title":"Data Science Programming AllinOne For Dummies","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eIntroduction \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbout This Book 1\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 3\u003c\/p\u003e \u003cp\u003eIcons Used in This Book 4\u003c\/p\u003e \u003cp\u003eBeyond the Book 4\u003c\/p\u003e \u003cp\u003eWhere to Go from Here 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBook 1: Defining Data Science\u003c\/b\u003e\u003cb\u003e 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Considering the History and Uses of Data Science\u003c\/b\u003e\u003cb\u003e 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the Elements of Data Science 10\u003c\/p\u003e \u003cp\u003eConsidering the emergence of data science 10\u003c\/p\u003e \u003cp\u003eOutlining the core competencies of a data scientist 11\u003c\/p\u003e \u003cp\u003eLinking data science, big data, and AI 12\u003c\/p\u003e \u003cp\u003eUnderstanding the role of programming 12\u003c\/p\u003e \u003cp\u003eDefining the Role of Data in the World 13\u003c\/p\u003e \u003cp\u003eEnticing people to buy products 13\u003c\/p\u003e \u003cp\u003eKeeping people safer 14\u003c\/p\u003e \u003cp\u003eCreating new technologies 15\u003c\/p\u003e \u003cp\u003ePerforming analysis for research 16\u003c\/p\u003e \u003cp\u003eProviding art and entertainment 17\u003c\/p\u003e \u003cp\u003eMaking life more interesting in other ways 18\u003c\/p\u003e \u003cp\u003eCreating the Data Science Pipeline 18\u003c\/p\u003e \u003cp\u003ePreparing the data 18\u003c\/p\u003e \u003cp\u003ePerforming exploratory data analysis 18\u003c\/p\u003e \u003cp\u003eLearning from data 19\u003c\/p\u003e \u003cp\u003eVisualizing 19\u003c\/p\u003e \u003cp\u003eObtaining insights and data products 19\u003c\/p\u003e \u003cp\u003eComparing Different Languages Used for Data Science 20\u003c\/p\u003e \u003cp\u003eObtaining an overview of data science languages 20\u003c\/p\u003e \u003cp\u003eDefining the pros and cons of using Python 22\u003c\/p\u003e \u003cp\u003eDefining the pros and cons of using R 23\u003c\/p\u003e \u003cp\u003eLearning to Perform Data Science Tasks Fast 25\u003c\/p\u003e \u003cp\u003eLoading data 26\u003c\/p\u003e \u003cp\u003eTraining a model 26\u003c\/p\u003e \u003cp\u003eViewing a result 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Placing Data Science within the Realm of AI\u003c\/b\u003e\u003cb\u003e 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSeeing the Data to Data Science Relationship 30\u003c\/p\u003e \u003cp\u003eConsidering the data architecture 30\u003c\/p\u003e \u003cp\u003eAcquiring data from various sources 31\u003c\/p\u003e \u003cp\u003ePerforming data analysis 32\u003c\/p\u003e \u003cp\u003eArchiving the data 33\u003c\/p\u003e \u003cp\u003eDefining the Levels of AI 33\u003c\/p\u003e \u003cp\u003eBeginning with AI 34\u003c\/p\u003e \u003cp\u003eAdvancing to machine learning 39\u003c\/p\u003e \u003cp\u003eGetting detailed with deep learning 43\u003c\/p\u003e \u003cp\u003eCreating a Pipeline from Data to AI 47\u003c\/p\u003e \u003cp\u003eConsidering the desired output 47\u003c\/p\u003e \u003cp\u003eDefining a data architecture 47\u003c\/p\u003e \u003cp\u003eCombining various data sources 47\u003c\/p\u003e \u003cp\u003eChecking for errors and fixing them 48\u003c\/p\u003e \u003cp\u003ePerforming the analysis 48\u003c\/p\u003e \u003cp\u003eValidating the result 49\u003c\/p\u003e \u003cp\u003eEnhancing application performance 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Creating a Data Science Lab of Your Own\u003c\/b\u003e\u003cb\u003e 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the Analysis Platform Options 52\u003c\/p\u003e \u003cp\u003eUsing a desktop system 53\u003c\/p\u003e \u003cp\u003eWorking with an online IDE 53\u003c\/p\u003e \u003cp\u003eConsidering the need for a GPU 54\u003c\/p\u003e \u003cp\u003eChoosing a Development Language 56\u003c\/p\u003e \u003cp\u003eObtaining and Using Python 58\u003c\/p\u003e \u003cp\u003eWorking with Python in this book 58\u003c\/p\u003e \u003cp\u003eObtaining and installing Anaconda for Python 59\u003c\/p\u003e \u003cp\u003eDefining a Python code repository 64\u003c\/p\u003e \u003cp\u003eWorking with Python using Google Colaboratory 69\u003c\/p\u003e \u003cp\u003eDefining the limits of using Azure Notebooks with Python and R 71\u003c\/p\u003e \u003cp\u003eObtaining and Using R 72\u003c\/p\u003e \u003cp\u003eObtaining and installing Anaconda for R 72\u003c\/p\u003e \u003cp\u003eStarting the R environment 73\u003c\/p\u003e \u003cp\u003eDefining an R code repository 75\u003c\/p\u003e \u003cp\u003ePresenting Frameworks 76\u003c\/p\u003e \u003cp\u003eDefining the differences 76\u003c\/p\u003e \u003cp\u003eExplaining the popularity of frameworks 77\u003c\/p\u003e \u003cp\u003eChoosing a particular library 79\u003c\/p\u003e \u003cp\u003eAccessing the Downloadable Code 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Considering Additional Packages and Libraries You Might Want\u003c\/b\u003e\u003cb\u003e 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the Uses for Third-Party Code 82\u003c\/p\u003e \u003cp\u003eObtaining Useful Python Packages 83\u003c\/p\u003e \u003cp\u003eAccessing scientific tools using SciPy 84\u003c\/p\u003e \u003cp\u003ePerforming fundamental scientific computing using NumPy 85\u003c\/p\u003e \u003cp\u003ePerforming data analysis using pandas 85\u003c\/p\u003e \u003cp\u003eImplementing machine learning using Scikit-learn 86\u003c\/p\u003e \u003cp\u003eGoing for deep learning with Keras and TensorFlow 86\u003c\/p\u003e \u003cp\u003ePlotting the data using matplotlib 87\u003c\/p\u003e \u003cp\u003eCreating graphs with NetworkX 88\u003c\/p\u003e \u003cp\u003eParsing HTML documents using Beautiful Soup 88\u003c\/p\u003e \u003cp\u003eLocating Useful R Libraries 89\u003c\/p\u003e \u003cp\u003eUsing your Python code in R with reticulate 89\u003c\/p\u003e \u003cp\u003eConducting advanced training using caret 90\u003c\/p\u003e \u003cp\u003ePerforming machine learning tasks using mlr 90\u003c\/p\u003e \u003cp\u003eVisualizing data using ggplot2 91\u003c\/p\u003e \u003cp\u003eEnhancing ggplot2 using esquisse 91\u003c\/p\u003e \u003cp\u003eCreating graphs with igraph 91\u003c\/p\u003e \u003cp\u003eParsing HTML documents using rvest 92\u003c\/p\u003e \u003cp\u003eWrangling dates using lubridate 92\u003c\/p\u003e \u003cp\u003eMaking big data simpler using dplyr and purrr 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Leveraging a Deep Learning Framework\u003c\/b\u003e\u003cb\u003e 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Deep Learning Framework Usage 96\u003c\/p\u003e \u003cp\u003eWorking with Low-End Frameworks 97\u003c\/p\u003e \u003cp\u003eChainer 97\u003c\/p\u003e \u003cp\u003ePyTorch 98\u003c\/p\u003e \u003cp\u003eMXNet 98\u003c\/p\u003e \u003cp\u003eMicrosoft Cognitive Toolkit\/CNTK 99\u003c\/p\u003e \u003cp\u003eUnderstanding TensorFlow 100\u003c\/p\u003e \u003cp\u003eGrasping why TensorFlow is so good 101\u003c\/p\u003e \u003cp\u003eMaking TensorFlow easier by using TFLearn 102\u003c\/p\u003e \u003cp\u003eUsing Keras as the best simplifier 102\u003c\/p\u003e \u003cp\u003eGetting your copy of TensorFlow and Keras 103\u003c\/p\u003e \u003cp\u003eFixing the C++ build tools error in Windows 106\u003c\/p\u003e \u003cp\u003eAccessing your new environment in Notebook 108\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBook 2: Interacting with Data Storage\u003c\/b\u003e\u003cb\u003e 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Manipulating Raw Data\u003c\/b\u003e\u003cb\u003e 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining the Data Sources 112\u003c\/p\u003e \u003cp\u003eObtaining data locally 112\u003c\/p\u003e \u003cp\u003eUsing online data sources 117\u003c\/p\u003e \u003cp\u003eEmploying dynamic data sources 121\u003c\/p\u003e \u003cp\u003eConsidering other kinds of data sources 123\u003c\/p\u003e \u003cp\u003eConsidering the Data Forms 124\u003c\/p\u003e \u003cp\u003eWorking with pure text 124\u003c\/p\u003e \u003cp\u003eAccessing formatted text 125\u003c\/p\u003e \u003cp\u003eDeciphering binary data 126\u003c\/p\u003e \u003cp\u003eUnderstanding the Need for Data Reliability 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Using Functional Programming Techniques\u003c\/b\u003e\u003cb\u003e 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Functional Programming 132\u003c\/p\u003e \u003cp\u003eDifferences with other programming paradigms 132\u003c\/p\u003e \u003cp\u003eUnderstanding its goals 133\u003c\/p\u003e \u003cp\u003eUnderstanding Pure and Impure Languages 134\u003c\/p\u003e \u003cp\u003eUsing the pure approach 134\u003c\/p\u003e \u003cp\u003eUsing the impure approach 134\u003c\/p\u003e \u003cp\u003eComparing the Functional Paradigm 135\u003c\/p\u003e \u003cp\u003eImperative 135\u003c\/p\u003e \u003cp\u003eProcedural 136\u003c\/p\u003e \u003cp\u003eObject-oriented 136\u003c\/p\u003e \u003cp\u003eDeclarative 136\u003c\/p\u003e \u003cp\u003eUsing Python for Functional Programming Needs 137\u003c\/p\u003e \u003cp\u003eUnderstanding How Functional Data Works 138\u003c\/p\u003e \u003cp\u003eWorking with immutable data 139\u003c\/p\u003e \u003cp\u003eConsidering the role of state 139\u003c\/p\u003e \u003cp\u003eEliminating side effects 140\u003c\/p\u003e \u003cp\u003ePassing by reference versus by value 140\u003c\/p\u003e \u003cp\u003eWorking with Lists and Strings 142\u003c\/p\u003e \u003cp\u003eCreating lists 144\u003c\/p\u003e \u003cp\u003eEvaluating lists 144\u003c\/p\u003e \u003cp\u003ePerforming common list manipulations 146\u003c\/p\u003e \u003cp\u003eUnderstanding the Dict and Set alternatives 147\u003c\/p\u003e \u003cp\u003eConsidering the use of strings 148\u003c\/p\u003e \u003cp\u003eEmploying Pattern Matching 150\u003c\/p\u003e \u003cp\u003eLooking for patterns in data 150\u003c\/p\u003e \u003cp\u003eUnderstanding regular expressions 152\u003c\/p\u003e \u003cp\u003eUsing pattern matching in analysis 155\u003c\/p\u003e \u003cp\u003eWorking with pattern matching 156\u003c\/p\u003e \u003cp\u003eWorking with Recursion 159\u003c\/p\u003e \u003cp\u003ePerforming tasks more than once 159\u003c\/p\u003e \u003cp\u003eUnderstanding recursion 161\u003c\/p\u003e \u003cp\u003eUsing recursion on lists 162\u003c\/p\u003e \u003cp\u003eConsidering advanced recursive tasks 163\u003c\/p\u003e \u003cp\u003ePassing functions instead of variables 164\u003c\/p\u003e \u003cp\u003ePerforming Functional Data Manipulation 165\u003c\/p\u003e \u003cp\u003eSlicing and dicing 166\u003c\/p\u003e \u003cp\u003eMapping your data 167\u003c\/p\u003e \u003cp\u003eFiltering data 168\u003c\/p\u003e \u003cp\u003eOrganizing data 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Working with Scalars, Vectors, and Matrices\u003c\/b\u003e\u003cb\u003e 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the Data Forms 172\u003c\/p\u003e \u003cp\u003eDefining Data Type through Scalars 173\u003c\/p\u003e \u003cp\u003eCreating Organized Data with Vectors 174\u003c\/p\u003e \u003cp\u003eDefining a vector 175\u003c\/p\u003e \u003cp\u003eCreating vectors of a specific type 175\u003c\/p\u003e \u003cp\u003ePerforming math on vectors 176\u003c\/p\u003e \u003cp\u003ePerforming logical and comparison tasks on vectors 176\u003c\/p\u003e \u003cp\u003eMultiplying vectors 177\u003c\/p\u003e \u003cp\u003eCreating and Using Matrices 178\u003c\/p\u003e \u003cp\u003eCreating a matrix 178\u003c\/p\u003e \u003cp\u003eCreating matrices of a specific type 179\u003c\/p\u003e \u003cp\u003eUsing the matrix class 181\u003c\/p\u003e \u003cp\u003ePerforming matrix multiplication 181\u003c\/p\u003e \u003cp\u003eExecuting advanced matrix operations 183\u003c\/p\u003e \u003cp\u003eExtending Analysis to Tensors 185\u003c\/p\u003e \u003cp\u003eUsing Vectorization Effectively 186\u003c\/p\u003e \u003cp\u003eSelecting and Shaping Data 187\u003c\/p\u003e \u003cp\u003eSlicing rows 188\u003c\/p\u003e \u003cp\u003eSlicing columns 188\u003c\/p\u003e \u003cp\u003eDicing 189\u003c\/p\u003e \u003cp\u003eConcatenating 189\u003c\/p\u003e \u003cp\u003eAggregating 194\u003c\/p\u003e \u003cp\u003eWorking with Trees 195\u003c\/p\u003e \u003cp\u003eUnderstanding the basics of trees 195\u003c\/p\u003e \u003cp\u003eBuilding a tree 196\u003c\/p\u003e \u003cp\u003eRepresenting Relations in a Graph 198\u003c\/p\u003e \u003cp\u003eGoing beyond trees 198\u003c\/p\u003e \u003cp\u003eArranging graphs 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Accessing Data in Files\u003c\/b\u003e\u003cb\u003e 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Flat File Data Sources 202\u003c\/p\u003e \u003cp\u003eWorking with Positional Data Files 203\u003c\/p\u003e \u003cp\u003eAccessing Data in CSV Files 205\u003c\/p\u003e \u003cp\u003eWorking with a simple CSV file 205\u003c\/p\u003e \u003cp\u003eMaking use of header information 208\u003c\/p\u003e \u003cp\u003eMoving On to XML Files 209\u003c\/p\u003e \u003cp\u003eWorking with a simple XML file 209\u003c\/p\u003e \u003cp\u003eParsing XML 211\u003c\/p\u003e \u003cp\u003eUsing XPath for data extraction 212\u003c\/p\u003e \u003cp\u003eConsidering Other Flat-File Data Sources 214\u003c\/p\u003e \u003cp\u003eWorking with Nontext Data 215\u003c\/p\u003e \u003cp\u003eDownloading Online Datasets 218\u003c\/p\u003e \u003cp\u003eWorking with package datasets 218\u003c\/p\u003e \u003cp\u003eUsing public domain datasets 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Working with a Relational DBMS\u003c\/b\u003e\u003cb\u003e 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering RDBMS Issues 224\u003c\/p\u003e \u003cp\u003eDefining the use of tables 225\u003c\/p\u003e \u003cp\u003eUnderstanding keys and indexes 226\u003c\/p\u003e \u003cp\u003eUsing local versus online databases 227\u003c\/p\u003e \u003cp\u003eWorking in read-only mode 228\u003c\/p\u003e \u003cp\u003eAccessing the RDBMS Data 228\u003c\/p\u003e \u003cp\u003eUsing the SQL language 229\u003c\/p\u003e \u003cp\u003eRelying on scripts 231\u003c\/p\u003e \u003cp\u003eRelying on views 231\u003c\/p\u003e \u003cp\u003eRelying on functions 232\u003c\/p\u003e \u003cp\u003eCreating a Dataset 233\u003c\/p\u003e \u003cp\u003eCombining data from multiple tables 233\u003c\/p\u003e \u003cp\u003eEnsuring data completeness 234\u003c\/p\u003e \u003cp\u003eSlicing and dicing the data as needed 234\u003c\/p\u003e \u003cp\u003eMixing RDBMS Products 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Working with a NoSQL DMBS\u003c\/b\u003e\u003cb\u003e 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the Ramifications of Hierarchical Data 238\u003c\/p\u003e \u003cp\u003eUnderstanding hierarchical organization 238\u003c\/p\u003e \u003cp\u003eDeveloping strategies for freeform data 239\u003c\/p\u003e \u003cp\u003ePerforming an analysis 240\u003c\/p\u003e \u003cp\u003eWorking around dangling data 241\u003c\/p\u003e \u003cp\u003eAccessing the Data 243\u003c\/p\u003e \u003cp\u003eCreating a picture of the data form 243\u003c\/p\u003e \u003cp\u003eEmploying the correct transiting strategy 244\u003c\/p\u003e \u003cp\u003eOrdering the data 247\u003c\/p\u003e \u003cp\u003eInteracting with Data from NoSQL Databases 248\u003c\/p\u003e \u003cp\u003eWorking with Dictionaries 249\u003c\/p\u003e \u003cp\u003eDeveloping Datasets from Hierarchical Data 250\u003c\/p\u003e \u003cp\u003eProcessing Hierarchical Data into Other Forms 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBook 3: Manipulating Data Using Basic Algorithms\u003c\/b\u003e\u003cb\u003e 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Working with Linear Regression\u003c\/b\u003e\u003cb\u003e 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the History of Linear Regression 256\u003c\/p\u003e \u003cp\u003eCombining Variables 257\u003c\/p\u003e \u003cp\u003eWorking through simple linear regression 257\u003c\/p\u003e \u003cp\u003eAdvancing to multiple linear regression 260\u003c\/p\u003e \u003cp\u003eConsidering which question to ask 262\u003c\/p\u003e \u003cp\u003eReducing independent variable complexity 263\u003c\/p\u003e \u003cp\u003eManipulating Categorical Variables 265\u003c\/p\u003e \u003cp\u003eCreating categorical variables 266\u003c\/p\u003e \u003cp\u003eRenaming levels 267\u003c\/p\u003e \u003cp\u003eCombining levels 268\u003c\/p\u003e \u003cp\u003eUsing Linear Regression to Guess Numbers 269\u003c\/p\u003e \u003cp\u003eDefining the family of linear models 270\u003c\/p\u003e \u003cp\u003eUsing more variables in a larger dataset 271\u003c\/p\u003e \u003cp\u003eUnderstanding variable transformations 274\u003c\/p\u003e \u003cp\u003eDoing variable transformations 275\u003c\/p\u003e \u003cp\u003eCreating interactions between variables 277\u003c\/p\u003e \u003cp\u003eUnderstanding limitations and problems 282\u003c\/p\u003e \u003cp\u003eLearning One Example at a Time 283\u003c\/p\u003e \u003cp\u003eUsing Gradient Descent 283\u003c\/p\u003e \u003cp\u003eImplementing Stochastic Gradient Descent 283\u003c\/p\u003e \u003cp\u003eConsidering the effects of regularization 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Moving Forward with Logistic Regression\u003c\/b\u003e\u003cb\u003e 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the History of Logistic Regression 290\u003c\/p\u003e \u003cp\u003eDifferentiating between Linear and Logistic Regression 291\u003c\/p\u003e \u003cp\u003eConsidering the model 291\u003c\/p\u003e \u003cp\u003eDefining the logistic function 292\u003c\/p\u003e \u003cp\u003eUnderstanding the problems that logistic regression solves 294\u003c\/p\u003e \u003cp\u003eFitting the curve 295\u003c\/p\u003e \u003cp\u003eConsidering a pass\/fail example 296\u003c\/p\u003e \u003cp\u003eUsing Logistic Regression to Guess Classes 297\u003c\/p\u003e \u003cp\u003eApplying logistic regression 297\u003c\/p\u003e \u003cp\u003eConsidering when classes are more 298\u003c\/p\u003e \u003cp\u003eDefining logistic regression performance 300\u003c\/p\u003e \u003cp\u003eSwitching to Probabilities 301\u003c\/p\u003e \u003cp\u003eSpecifying a binary response 301\u003c\/p\u003e \u003cp\u003eTransforming numeric estimates into probabilities 302\u003c\/p\u003e \u003cp\u003eWorking through Multiclass Regression 305\u003c\/p\u003e \u003cp\u003eUnderstanding multiclass regression 305\u003c\/p\u003e \u003cp\u003eDeveloping a multiclass regression implementation 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Predicting Outcomes Using Bayes\u003c\/b\u003e\u003cb\u003e 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Bayes’ Theorem 310\u003c\/p\u003e \u003cp\u003eDelving into Bayes history 310\u003c\/p\u003e \u003cp\u003eConsidering the basic theorem 312\u003c\/p\u003e \u003cp\u003eUsing Naïve Bayes for Predictions 313\u003c\/p\u003e \u003cp\u003eFinding out that Naïve Bayes isn’t so naïve 314\u003c\/p\u003e \u003cp\u003ePredicting text classifications 315\u003c\/p\u003e \u003cp\u003eGetting an overview of Bayesian inference 318\u003c\/p\u003e \u003cp\u003eWorking with Networked Bayes 324\u003c\/p\u003e \u003cp\u003eConsidering the network types and uses 324\u003c\/p\u003e \u003cp\u003eUnderstanding Directed Acyclic Graphs (DAGs) 327\u003c\/p\u003e \u003cp\u003eEmploying networked Bayes in predictions 328\u003c\/p\u003e \u003cp\u003eDeciding between automated and guided learning 332\u003c\/p\u003e \u003cp\u003eConsidering the Use of Bayesian Linear Regression 332\u003c\/p\u003e \u003cp\u003eConsidering the Use of Bayesian Logistic Regression 333\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Learning with K-Nearest Neighbors\u003c\/b\u003e\u003cb\u003e 335\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the History of K-Nearest Neighbors 336\u003c\/p\u003e \u003cp\u003eLearning Lazily with K-Nearest Neighbors 337\u003c\/p\u003e \u003cp\u003eUnderstanding the basis of KNN 337\u003c\/p\u003e \u003cp\u003ePredicting after observing neighbors 338\u003c\/p\u003e \u003cp\u003eChoosing the k parameter wisely 341\u003c\/p\u003e \u003cp\u003eLeveraging the Correct k Parameter 342\u003c\/p\u003e \u003cp\u003eUnderstanding the k parameter 342\u003c\/p\u003e \u003cp\u003eExperimenting with a flexible algorithm 343\u003c\/p\u003e \u003cp\u003eImplementing KNN Regression 345\u003c\/p\u003e \u003cp\u003eImplementing KNN Classification 347\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBook 4: Performing Advanced Data Manipulation\u003c\/b\u003e\u003cb\u003e 351\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Leveraging Ensembles of Learners\u003c\/b\u003e\u003cb\u003e 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLeveraging Decision Trees 354\u003c\/p\u003e \u003cp\u003eGrowing a forest of trees 356\u003c\/p\u003e \u003cp\u003eSeeing Random Forests in action 358\u003c\/p\u003e \u003cp\u003eUnderstanding the importance measures 360\u003c\/p\u003e \u003cp\u003eConfiguring your system for importance measures with Python 361\u003c\/p\u003e \u003cp\u003eSeeing importance measures in action 361\u003c\/p\u003e \u003cp\u003eWorking with Almost Random Guesses 364\u003c\/p\u003e \u003cp\u003eUnderstanding the premise 365\u003c\/p\u003e \u003cp\u003eBagging predictors with AdaBoost 366\u003c\/p\u003e \u003cp\u003eMeeting Again with Gradient Descent 369\u003c\/p\u003e \u003cp\u003eUnderstanding the GBM difference 369\u003c\/p\u003e \u003cp\u003eSeeing GBM in action 371\u003c\/p\u003e \u003cp\u003eAveraging Different Predictors 372\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Building Deep Learning Models\u003c\/b\u003e\u003cb\u003e 373\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDiscovering the Incredible Perceptron 374\u003c\/p\u003e \u003cp\u003eUnderstanding perceptron functionality 375\u003c\/p\u003e \u003cp\u003eTouching the nonseparability limit 376\u003c\/p\u003e \u003cp\u003eHitting Complexity with Neural Networks 378\u003c\/p\u003e \u003cp\u003eConsidering the neuron 379\u003c\/p\u003e \u003cp\u003ePushing data with feed-forward 381\u003c\/p\u003e \u003cp\u003eDefining hidden layers 383\u003c\/p\u003e \u003cp\u003eExecuting operations 384\u003c\/p\u003e \u003cp\u003eConsidering the details of data movement through the neural network 386\u003c\/p\u003e \u003cp\u003eUsing backpropagation to adjust learning 387\u003c\/p\u003e \u003cp\u003eUnderstanding More about Neural Networks 390\u003c\/p\u003e \u003cp\u003eGetting an overview of the neural network process 391\u003c\/p\u003e \u003cp\u003eDefining the basic architecture 391\u003c\/p\u003e \u003cp\u003eDocumenting the essential modules 393\u003c\/p\u003e \u003cp\u003eSolving a simple problem 396\u003c\/p\u003e \u003cp\u003eLooking Under the Hood of Neural Networks 399\u003c\/p\u003e \u003cp\u003eChoosing the right activation function 399\u003c\/p\u003e \u003cp\u003eRelying on a smart optimizer 401\u003c\/p\u003e \u003cp\u003eSetting a working learning rate 402\u003c\/p\u003e \u003cp\u003eExplaining Deep Learning Differences with Other Forms of AI 402\u003c\/p\u003e \u003cp\u003eAdding more layers 403\u003c\/p\u003e \u003cp\u003eChanging the activations 405\u003c\/p\u003e \u003cp\u003eAdding regularization by dropout 406\u003c\/p\u003e \u003cp\u003eUsing online learning 407\u003c\/p\u003e \u003cp\u003eTransferring learning 407\u003c\/p\u003e \u003cp\u003eLearning end to end 408\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Recognizing Images with CNNs\u003c\/b\u003e\u003cb\u003e 409\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBeginning with Simple Image Recognition 410\u003c\/p\u003e \u003cp\u003eConsidering the ramifications of sight 410\u003c\/p\u003e \u003cp\u003eWorking with a set of images 411\u003c\/p\u003e \u003cp\u003eExtracting visual features 417\u003c\/p\u003e \u003cp\u003eRecognizing faces using Eigenfaces 419\u003c\/p\u003e \u003cp\u003eClassifying images 423\u003c\/p\u003e \u003cp\u003eUnderstanding CNN Image Basics 427\u003c\/p\u003e \u003cp\u003eMoving to CNNs with Character Recognition 429\u003c\/p\u003e \u003cp\u003eAccessing the dataset 430\u003c\/p\u003e \u003cp\u003eReshaping the dataset 431\u003c\/p\u003e \u003cp\u003eEncoding the categories 432\u003c\/p\u003e \u003cp\u003eDefining the model 432\u003c\/p\u003e \u003cp\u003eUsing the model 433\u003c\/p\u003e \u003cp\u003eExplaining How Convolutions Work 435\u003c\/p\u003e \u003cp\u003eUnderstanding convolutions 435\u003c\/p\u003e \u003cp\u003eSimplifying the use of pooling 439\u003c\/p\u003e \u003cp\u003eDescribing the LeNet architecture 440\u003c\/p\u003e \u003cp\u003eDetecting Edges and Shapes from Images 446\u003c\/p\u003e \u003cp\u003eVisualizing convolutions 447\u003c\/p\u003e \u003cp\u003eUnveiling successful architectures 449\u003c\/p\u003e \u003cp\u003eDiscussing transfer learning 450\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Processing Text and Other Sequences\u003c\/b\u003e\u003cb\u003e 453\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Natural Language Processing 454\u003c\/p\u003e \u003cp\u003eDefining the human perspective as it relates to data science 454\u003c\/p\u003e \u003cp\u003eConsidering the computer perspective as it relates to data science 455\u003c\/p\u003e \u003cp\u003eUnderstanding How Machines Read 456\u003c\/p\u003e \u003cp\u003eCreating a corpus 457\u003c\/p\u003e \u003cp\u003ePerforming feature extraction 457\u003c\/p\u003e \u003cp\u003eUnderstanding the BoW 458\u003c\/p\u003e \u003cp\u003eProcessing and enhancing text 459\u003c\/p\u003e \u003cp\u003eMaintaining order using n-grams 461\u003c\/p\u003e \u003cp\u003eStemming and removing stop words 462\u003c\/p\u003e \u003cp\u003eScraping textual datasets from the web 465\u003c\/p\u003e \u003cp\u003eHandling problems with raw text 470\u003c\/p\u003e \u003cp\u003eStoring processed text data in sparse matrices 473\u003c\/p\u003e \u003cp\u003eUnderstanding Semantics Using Word Embeddings 478\u003c\/p\u003e \u003cp\u003eUsing Scoring and Classification 482\u003c\/p\u003e \u003cp\u003ePerforming classification tasks 482\u003c\/p\u003e \u003cp\u003eAnalyzing reviews from e-commerce 485\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBook 5: Performing Data-Related Tasks\u003c\/b\u003e\u003cb\u003e 491\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Making Recommendations\u003c\/b\u003e\u003cb\u003e 493\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRealizing the Recommendation Revolution 494\u003c\/p\u003e \u003cp\u003eDownloading Rating Data 495\u003c\/p\u003e \u003cp\u003eNavigating through anonymous web data 496\u003c\/p\u003e \u003cp\u003eEncountering the limits of rating data 499\u003c\/p\u003e \u003cp\u003eLeveraging SVD 506\u003c\/p\u003e \u003cp\u003eConsidering the origins of SVD 506\u003c\/p\u003e \u003cp\u003eUnderstanding the SVD connection 508\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Performing Complex Classifications\u003c\/b\u003e\u003cb\u003e 509\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing Image Classification Challenges 510\u003c\/p\u003e \u003cp\u003eDelving into ImageNet and Coco 511\u003c\/p\u003e \u003cp\u003eLearning the magic of data augmentation 513\u003c\/p\u003e \u003cp\u003eDistinguishing Traffic Signs 516\u003c\/p\u003e \u003cp\u003ePreparing the image data 517\u003c\/p\u003e \u003cp\u003eRunning a classification task 520\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Identifying Objects\u003c\/b\u003e\u003cb\u003e 525\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDistinguishing Classification Tasks 526\u003c\/p\u003e \u003cp\u003eUnderstanding the problem 526\u003c\/p\u003e \u003cp\u003ePerforming localization 527\u003c\/p\u003e \u003cp\u003eClassifying multiple objects 528\u003c\/p\u003e \u003cp\u003eAnnotating multiple objects in images 529\u003c\/p\u003e \u003cp\u003eSegmenting images 530\u003c\/p\u003e \u003cp\u003ePerceiving Objects in Their Surroundings 531\u003c\/p\u003e \u003cp\u003eConsidering vision needs in self-driving cars 531\u003c\/p\u003e \u003cp\u003eDiscovering how RetinaNet works 532\u003c\/p\u003e \u003cp\u003eUsing the Keras-RetinaNet code 534\u003c\/p\u003e \u003cp\u003eOvercoming Adversarial Attacks on Deep Learning Applications 538\u003c\/p\u003e \u003cp\u003eTricking pixels 539\u003c\/p\u003e \u003cp\u003eHacking with stickers and other artifacts 541\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Analyzing Music and Video \u003c\/b\u003e\u003cb\u003e543\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLearning to Imitate Art and Life 544\u003c\/p\u003e \u003cp\u003eTransferring an artistic style 545\u003c\/p\u003e \u003cp\u003eReducing the problem to statistics 546\u003c\/p\u003e \u003cp\u003eUnderstanding that deep learning doesn’t create 548\u003c\/p\u003e \u003cp\u003eMimicking an Artist 548\u003c\/p\u003e \u003cp\u003eDefining a new piece based on a single artist 549\u003c\/p\u003e \u003cp\u003eCombining styles to create new art 550\u003c\/p\u003e \u003cp\u003eVisualizing how neural networks dream 551\u003c\/p\u003e \u003cp\u003eUsing a network to compose music 551\u003c\/p\u003e \u003cp\u003eOther creative avenues 552\u003c\/p\u003e \u003cp\u003eMoving toward GANs 553\u003c\/p\u003e \u003cp\u003eFinding the key in the competition 554\u003c\/p\u003e \u003cp\u003eConsidering a growing field 556\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Considering Other Task Types\u003c\/b\u003e\u003cb\u003e 559\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProcessing Language in Texts 560\u003c\/p\u003e \u003cp\u003eConsidering the processing methodologies 560\u003c\/p\u003e \u003cp\u003eDefining understanding as tokenization 561\u003c\/p\u003e \u003cp\u003ePutting all the documents into a bag 562\u003c\/p\u003e \u003cp\u003eUsing AI for sentiment analysis 566\u003c\/p\u003e \u003cp\u003eProcessing Time Series 574\u003c\/p\u003e \u003cp\u003eDefining sequences of events 574\u003c\/p\u003e \u003cp\u003ePerforming a prediction using LSTM 575\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Developing Impressive Charts and Plots\u003c\/b\u003e\u003cb\u003e 579\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStarting a Graph, Chart, or Plot 580\u003c\/p\u003e \u003cp\u003eUnderstanding the differences between graphs, charts, and plots 580\u003c\/p\u003e \u003cp\u003eConsidering the graph, chart, and plot types 582\u003c\/p\u003e \u003cp\u003eDefining the plot 583\u003c\/p\u003e \u003cp\u003eDrawing multiple lines 584\u003c\/p\u003e \u003cp\u003eDrawing multiple plots 584\u003c\/p\u003e \u003cp\u003eSaving your work 586\u003c\/p\u003e \u003cp\u003eSetting the Axis, Ticks, and Grids 587\u003c\/p\u003e \u003cp\u003eGetting the axis 587\u003c\/p\u003e \u003cp\u003eFormatting the ticks 590\u003c\/p\u003e \u003cp\u003eAdding grids 590\u003c\/p\u003e \u003cp\u003eDefining the Line Appearance 591\u003c\/p\u003e \u003cp\u003eWorking with line styles 592\u003c\/p\u003e \u003cp\u003eAdding markers 593\u003c\/p\u003e \u003cp\u003eUsing Labels, Annotations, and Legends 594\u003c\/p\u003e \u003cp\u003eAdding labels 595\u003c\/p\u003e \u003cp\u003eAnnotating the chart 596\u003c\/p\u003e \u003cp\u003eCreating a legend 598\u003c\/p\u003e \u003cp\u003eCreating Scatterplots 599\u003c\/p\u003e \u003cp\u003eDepicting groups 599\u003c\/p\u003e \u003cp\u003eShowing correlations 600\u003c\/p\u003e \u003cp\u003ePlotting Time Series 603\u003c\/p\u003e \u003cp\u003eRepresenting time on axes 604\u003c\/p\u003e \u003cp\u003ePlotting trends over time 605\u003c\/p\u003e \u003cp\u003ePlotting Geographical Data 608\u003c\/p\u003e \u003cp\u003eGetting the toolkit 608\u003c\/p\u003e \u003cp\u003eDrawing the map 609\u003c\/p\u003e \u003cp\u003ePlotting the data 613\u003c\/p\u003e \u003cp\u003eVisualizing Graphs 615\u003c\/p\u003e \u003cp\u003eUnderstanding the adjacency matrix 615\u003c\/p\u003e \u003cp\u003eUsing NetworkX basics 615\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBook 6: Diagnosing and Fixing Errors\u003c\/b\u003e\u003cb\u003e 619\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Locating Errors in Your Data\u003c\/b\u003e\u003cb\u003e 621\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the Types of Data Errors 622\u003c\/p\u003e \u003cp\u003eObtaining the Required Data 624\u003c\/p\u003e \u003cp\u003eConsidering the data sources 624\u003c\/p\u003e \u003cp\u003eObtaining reliable data 625\u003c\/p\u003e \u003cp\u003eMaking human input more reliable 626\u003c\/p\u003e \u003cp\u003eUsing automated data collection 628\u003c\/p\u003e \u003cp\u003eValidating Your Data 629\u003c\/p\u003e \u003cp\u003eFiguring out what’s in your data 629\u003c\/p\u003e \u003cp\u003eRemoving duplicates 631\u003c\/p\u003e \u003cp\u003eCreating a data map and a data plan 632\u003c\/p\u003e \u003cp\u003eManicuring the Data 634\u003c\/p\u003e \u003cp\u003eDealing with missing data 634\u003c\/p\u003e \u003cp\u003eConsidering data misalignments 639\u003c\/p\u003e \u003cp\u003eSeparating out useful data 640\u003c\/p\u003e \u003cp\u003eDealing with Dates in Your Data 640\u003c\/p\u003e \u003cp\u003eFormatting date and time values 641\u003c\/p\u003e \u003cp\u003eUsing the right time transformation 641\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Considering Outrageous Outcomes\u003c\/b\u003e\u003cb\u003e 643\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDeciding What Outrageous Means 644\u003c\/p\u003e \u003cp\u003eConsidering the Five Mistruths in Data 645\u003c\/p\u003e \u003cp\u003eCommission 645\u003c\/p\u003e \u003cp\u003eOmission 646\u003c\/p\u003e \u003cp\u003ePerspective 646\u003c\/p\u003e \u003cp\u003eBias 647\u003c\/p\u003e \u003cp\u003eFrame-of-reference 648\u003c\/p\u003e \u003cp\u003eConsidering Detection of Outliers 649\u003c\/p\u003e \u003cp\u003eUnderstanding outlier basics 649\u003c\/p\u003e \u003cp\u003eFinding more things that can go wrong 651\u003c\/p\u003e \u003cp\u003eUnderstanding anomalies and novel data 651\u003c\/p\u003e \u003cp\u003eExamining a Simple Univariate Method 653\u003c\/p\u003e \u003cp\u003eUsing the pandas package 653\u003c\/p\u003e \u003cp\u003eLeveraging the Gaussian distribution 655\u003c\/p\u003e \u003cp\u003eMaking assumptions and checking out 656\u003c\/p\u003e \u003cp\u003eDeveloping a Multivariate Approach 657\u003c\/p\u003e \u003cp\u003eUsing principle component analysis 658\u003c\/p\u003e \u003cp\u003eUsing cluster analysis 659\u003c\/p\u003e \u003cp\u003eAutomating outliers detection with Isolation Forests 661\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Dealing with Model Overfitting and Underfitting\u003c\/b\u003e\u003cb\u003e 663\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding the Causes 664\u003c\/p\u003e \u003cp\u003eConsidering the problem 664\u003c\/p\u003e \u003cp\u003eLooking at underfitting 665\u003c\/p\u003e \u003cp\u003eLooking at overfitting 666\u003c\/p\u003e \u003cp\u003ePlotting learning curves for insights 668\u003c\/p\u003e \u003cp\u003eDetermining the Sources of Overfitting and Underfitting 670\u003c\/p\u003e \u003cp\u003eUnderstanding bias and variance 671\u003c\/p\u003e \u003cp\u003eHaving insufficient data 671\u003c\/p\u003e \u003cp\u003eBeing fooled by data leakage 672\u003c\/p\u003e \u003cp\u003eGuessing the Right Features 672\u003c\/p\u003e \u003cp\u003eSelecting variables like a pro 673\u003c\/p\u003e \u003cp\u003eUsing nonlinear transformations 676\u003c\/p\u003e \u003cp\u003eRegularizing linear models 684\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Obtaining the Correct Output Presentation\u003c\/b\u003e\u003cb\u003e 689\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConsidering the Meaning of Correct 690\u003c\/p\u003e \u003cp\u003eDetermining a Presentation Type 691\u003c\/p\u003e \u003cp\u003eConsidering the audience 691\u003c\/p\u003e \u003cp\u003eDefining a depth of detail 692\u003c\/p\u003e \u003cp\u003eEnsuring that the data is consistent with audience needs 693\u003c\/p\u003e \u003cp\u003eUnderstanding timeliness 693\u003c\/p\u003e \u003cp\u003eChoosing the Right Graph 694\u003c\/p\u003e \u003cp\u003eTelling a story with your graphs 694\u003c\/p\u003e \u003cp\u003eShowing parts of a whole with pie charts 694\u003c\/p\u003e \u003cp\u003eCreating comparisons with bar charts 695\u003c\/p\u003e \u003cp\u003eShowing distributions using histograms 697\u003c\/p\u003e \u003cp\u003eDepicting groups using boxplots 699\u003c\/p\u003e \u003cp\u003eDefining a data flow using line graphs 700\u003c\/p\u003e \u003cp\u003eSeeing data patterns using scatterplots 701\u003c\/p\u003e \u003cp\u003eWorking with External Data 702\u003c\/p\u003e \u003cp\u003eEmbedding plots and other images 703\u003c\/p\u003e \u003cp\u003eLoading examples from online sites 703\u003c\/p\u003e \u003cp\u003eObtaining online graphics and multimedia 704\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Developing Consistent Strategies\u003c\/b\u003e\u003cb\u003e 707\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStandardizing Data Collection Techniques 707\u003c\/p\u003e \u003cp\u003eUsing Reliable Sources 709\u003c\/p\u003e \u003cp\u003eVerifying Dynamic Data Sources 711\u003c\/p\u003e \u003cp\u003eConsidering the problem 712\u003c\/p\u003e \u003cp\u003eAnalyzing streams with the right recipe 714\u003c\/p\u003e \u003cp\u003eLooking for New Data Collection Trends 715\u003c\/p\u003e \u003cp\u003eWeeding Old Data 716\u003c\/p\u003e \u003cp\u003eConsidering the Need for Randomness 717\u003c\/p\u003e \u003cp\u003eConsidering why randomization is needed 718\u003c\/p\u003e \u003cp\u003eUnderstanding how probability works 718\u003c\/p\u003e \u003cp\u003eIndex 721\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407102583127,"sku":"9781119626114","price":26.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119626114.jpg?v=1730498186","url":"https:\/\/bookcurl.com\/products\/data-science-programming-allinone-for-dummies-9781119626114","provider":"Book Curl","version":"1.0","type":"link"}