{"product_id":"data-science-programming-all-in-one-for-dummies-isbn-9781119626114","title":"Data Science Programming All-in-One For Dummies","description":"\u003cp\u003e\u003cb\u003eYour logical, linear guide to the fundamentals of data science programming\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eData Science Programming All-In-One For Dummies\u003c\/i\u003e is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eGet grounded: the ideal start for new data professionals\u003c\/li\u003e \u003cli\u003eWhat lies ahead: learn about specific areas that data is transforming  \u003c\/li\u003e \u003cli\u003eBe meaningful: find out how to tell your data story\u003c\/li\u003e \u003cli\u003eSee clearly: pick up the art of visualization\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWhether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!\u003c\/p\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  \u003cp\u003e\u003cb\u003eJohn Mueller\u003c\/b\u003e has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). \u003cb\u003eLuca Massaron,\u003c\/b\u003e a Google Developer Expert (GDE),??interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eYour complete guide to data science programming\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003eThis friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear and logistic regression, ensembles of learners, deep neural networks, recommenders, optimization and validation of models. However, it isn't all about using math to manipulate the data. You use the math to perform the very same machine learning and deep learning tasks that make doctors more efficient, reduce traffic accidents and solve business problems, as well as many other problems of daily life. Knowing the math also enables you to do things like analyze image and audio data, create graphics that allow others to understand the data, and even possibly become an artist.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003e6 Books Inside…\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e \u003cli\u003eDefining Data Science\u003c\/li\u003e \u003cli\u003eInteracting with Data Storage\u003c\/li\u003e \u003cli\u003eManipulating Data Using Basic Algorithms\u003c\/li\u003e \u003cli\u003ePerforming Advanced Data Manipulation\u003c\/li\u003e \u003cli\u003ePerforming Data-related Tasks\u003c\/li\u003e \u003cli\u003eDiagnosing and Fixing Errors\u003c\/li\u003e \u003c\/ul\u003e","brand":"For Dummies","offers":[{"title":"Default Title","offer_id":47989025865957,"sku":"NP9781119626114","price":44.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119626114.jpg?v=1761782489","url":"https:\/\/k12savings.com\/products\/data-science-programming-all-in-one-for-dummies-isbn-9781119626114","provider":"K12savings","version":"1.0","type":"link"}