Mastering predictive analytics with Python :: exploit the power of data in your business by building advanced predictive modeling applications with Python /
Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking gui...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Birmingham, UK :
Packt Publishing,
[2016].
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| Schriftenreihe: | Community experience distilled.
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| Zusammenfassung: | Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn Gain an insight into components and design decisions for an analytical application Master the use Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover the advanced methods to analyze structured and unstructured data Find out how to deploy a machine learning model in a production environment Visualize the performance of models and the insights they produce Scale your solutions as your data grows using Python Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the ... |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781785889820 1785889826 |
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| 520 | |a Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn Gain an insight into components and design decisions for an analytical application Master the use Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover the advanced methods to analyze structured and unstructured data Find out how to deploy a machine learning model in a production environment Visualize the performance of models and the insights they produce Scale your solutions as your data grows using Python Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the ... | ||
| 505 | 0 | |a Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: From Data to Decisions -- Getting Started with Analytic Applications -- Designing an advanced analytic solution -- Data layer: warehouses, lakes, and streams -- Modeling layer -- Deployment layer -- Reporting layer -- Case study: sentiment analysis of social media feeds -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Case study: targeted e-mail campaigns -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Summary -- Chapter 2: Exploratory Data Analysis and Visualization in Python -- Exploring categorical and numerical data in IPython -- Installing IPython notebook -- The notebook interface -- Loading and inspecting data -- Basic manipulations -- grouping, filtering, mapping, and pivoting -- Charting with Matplotlib -- Time series analysis -- Cleaning and converting -- Time series diagnostics -- Joining signals and correlation -- Working with geospatial data -- Loading geospatial data -- Working in the cloud -- Introduction to PySpark -- Creating the SparkContext -- Creating an RDD -- Creating a Spark DataFrame -- Summary -- Chapter 3: Finding Patterns in the Noise -- Clustering and Unsupervised Learning -- Similarity and distance metrics -- Numerical distance metrics -- Correlation similarity metrics and time series -- Similarity metrics for categorical data -- K-means clustering -- Affinity propagation -- automatically choosing cluster numbers -- k-medoids -- Agglomerative clustering -- Where agglomerative clustering fails -- Streaming clustering in Spark -- Summary -- Chapter 4: Connecting the Dots with Models -- Regression Methods -- Linear regression -- Data preparation. | |
| 505 | 8 | |a Model fitting and evaluation -- Statistical significance of regression outputs -- Generalize estimating equations -- Mixed effects models -- Time series data -- Generalized linear models -- Applying regularization to linear models -- Tree methods -- Decision trees -- Random forest -- Scaling out with PySpark -- predicting year of song release -- Summary -- Chapter 5: Putting Data in its Place -- Classification Methods and Analysis -- Logistic regression -- Multiclass logistic classifiers: multinomial regression -- Formatting a dataset for classification problems -- Learning pointwise updates with stochastic gradient descent -- Jointly optimizing all parameters with second-order methods -- Fitting the model -- Evaluating classification models -- Strategies for improving classification models -- Separating Nonlinear boundaries with Support vector machines -- Fitting and SVM to the census data -- Boosting: combining small models to improve accuracy -- Gradient boosted decision trees -- Comparing classification methods -- Case study: fitting classifier models in pyspark -- Summary -- Chapter 6: Words and Pixels -- Working with Unstructured Data -- Working with textual data -- Cleaning textual data -- Extracting features from textual data -- Using dimensionality reduction to simplify datasets -- Principal component analysis -- Latent Dirichlet Allocation -- Using dimensionality reduction in predictive modeling -- Images -- Cleaning image data -- Thresholding images to highlight objects -- Dimensionality reduction for image analysis -- Case Study: Training a Recommender System in PySpark -- Summary -- Chapter 7: Learning from the Bottom Up -- Deep Networks and Unsupervised Features -- Learning patterns with neural networks -- A network of one -- the perceptron -- Combining perceptrons -- a single-layer neural network -- Parameter fitting with back-propagation. | |
| 505 | 8 | |a Discriminative versus generative models -- Vanishing gradients and explaining away -- Pretraining belief networks -- Using dropout to regularize networks -- Convolutional networks and rectified units -- Compressing Data with autoencoder networks -- Optimizing the learning rate -- The TensorFlow library and digit recognition -- The MNIST data -- Constructing the network -- Summary -- Chapter 8: Sharing Models with Prediction Services -- The architecture of a prediction service -- Clients and making requests -- The GET requests -- The POST request -- The HEAD request -- The PUT request -- The DELETE request -- Server -- the web traffic controller -- Application -- the engine of the predictive services -- Persisting information with database systems -- Case study -- logistic regression service -- Setting up the database -- The web server -- The web application -- The flow of a prediction service -- training a model -- On-demand and bulk prediction -- Summary -- Chapter 9: Reporting and Testing -- Iterating on Analytic Systems -- Checking the health of models with diagnostics -- Evaluating changes in model performance -- Changes in feature importance -- Changes in unsupervised model performance -- Iterating on models through A/B testing -- Experimental allocation -- assigning customers to experiments -- Deciding a sample size -- Multiple hypothesis testing -- Guidelines for communication -- Translate terms to business values -- Visualizing results -- Case Study: building a reporting service -- The report server -- The report application -- The visualization layer -- Summary -- Index. | |
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| contents | Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: From Data to Decisions -- Getting Started with Analytic Applications -- Designing an advanced analytic solution -- Data layer: warehouses, lakes, and streams -- Modeling layer -- Deployment layer -- Reporting layer -- Case study: sentiment analysis of social media feeds -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Case study: targeted e-mail campaigns -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Summary -- Chapter 2: Exploratory Data Analysis and Visualization in Python -- Exploring categorical and numerical data in IPython -- Installing IPython notebook -- The notebook interface -- Loading and inspecting data -- Basic manipulations -- grouping, filtering, mapping, and pivoting -- Charting with Matplotlib -- Time series analysis -- Cleaning and converting -- Time series diagnostics -- Joining signals and correlation -- Working with geospatial data -- Loading geospatial data -- Working in the cloud -- Introduction to PySpark -- Creating the SparkContext -- Creating an RDD -- Creating a Spark DataFrame -- Summary -- Chapter 3: Finding Patterns in the Noise -- Clustering and Unsupervised Learning -- Similarity and distance metrics -- Numerical distance metrics -- Correlation similarity metrics and time series -- Similarity metrics for categorical data -- K-means clustering -- Affinity propagation -- automatically choosing cluster numbers -- k-medoids -- Agglomerative clustering -- Where agglomerative clustering fails -- Streaming clustering in Spark -- Summary -- Chapter 4: Connecting the Dots with Models -- Regression Methods -- Linear regression -- Data preparation. Model fitting and evaluation -- Statistical significance of regression outputs -- Generalize estimating equations -- Mixed effects models -- Time series data -- Generalized linear models -- Applying regularization to linear models -- Tree methods -- Decision trees -- Random forest -- Scaling out with PySpark -- predicting year of song release -- Summary -- Chapter 5: Putting Data in its Place -- Classification Methods and Analysis -- Logistic regression -- Multiclass logistic classifiers: multinomial regression -- Formatting a dataset for classification problems -- Learning pointwise updates with stochastic gradient descent -- Jointly optimizing all parameters with second-order methods -- Fitting the model -- Evaluating classification models -- Strategies for improving classification models -- Separating Nonlinear boundaries with Support vector machines -- Fitting and SVM to the census data -- Boosting: combining small models to improve accuracy -- Gradient boosted decision trees -- Comparing classification methods -- Case study: fitting classifier models in pyspark -- Summary -- Chapter 6: Words and Pixels -- Working with Unstructured Data -- Working with textual data -- Cleaning textual data -- Extracting features from textual data -- Using dimensionality reduction to simplify datasets -- Principal component analysis -- Latent Dirichlet Allocation -- Using dimensionality reduction in predictive modeling -- Images -- Cleaning image data -- Thresholding images to highlight objects -- Dimensionality reduction for image analysis -- Case Study: Training a Recommender System in PySpark -- Summary -- Chapter 7: Learning from the Bottom Up -- Deep Networks and Unsupervised Features -- Learning patterns with neural networks -- A network of one -- the perceptron -- Combining perceptrons -- a single-layer neural network -- Parameter fitting with back-propagation. Discriminative versus generative models -- Vanishing gradients and explaining away -- Pretraining belief networks -- Using dropout to regularize networks -- Convolutional networks and rectified units -- Compressing Data with autoencoder networks -- Optimizing the learning rate -- The TensorFlow library and digit recognition -- The MNIST data -- Constructing the network -- Summary -- Chapter 8: Sharing Models with Prediction Services -- The architecture of a prediction service -- Clients and making requests -- The GET requests -- The POST request -- The HEAD request -- The PUT request -- The DELETE request -- Server -- the web traffic controller -- Application -- the engine of the predictive services -- Persisting information with database systems -- Case study -- logistic regression service -- Setting up the database -- The web server -- The web application -- The flow of a prediction service -- training a model -- On-demand and bulk prediction -- Summary -- Chapter 9: Reporting and Testing -- Iterating on Analytic Systems -- Checking the health of models with diagnostics -- Evaluating changes in model performance -- Changes in feature importance -- Changes in unsupervised model performance -- Iterating on models through A/B testing -- Experimental allocation -- assigning customers to experiments -- Deciding a sample size -- Multiple hypothesis testing -- Guidelines for communication -- Translate terms to business values -- Visualizing results -- Case Study: building a reporting service -- The report server -- The report application -- The visualization layer -- Summary -- Index. |
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You're expected to have basic development experience with Python. What You Will Learn Gain an insight into components and design decisions for an analytical application Master the use Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover the advanced methods to analyze structured and unstructured data Find out how to deploy a machine learning model in a production environment Visualize the performance of models and the insights they produce Scale your solutions as your data grows using Python Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. 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You will learn to choose the ...</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: From Data to Decisions -- Getting Started with Analytic Applications -- Designing an advanced analytic solution -- Data layer: warehouses, lakes, and streams -- Modeling layer -- Deployment layer -- Reporting layer -- Case study: sentiment analysis of social media feeds -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Case study: targeted e-mail campaigns -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Summary -- Chapter 2: Exploratory Data Analysis and Visualization in Python -- Exploring categorical and numerical data in IPython -- Installing IPython notebook -- The notebook interface -- Loading and inspecting data -- Basic manipulations -- grouping, filtering, mapping, and pivoting -- Charting with Matplotlib -- Time series analysis -- Cleaning and converting -- Time series diagnostics -- Joining signals and correlation -- Working with geospatial data -- Loading geospatial data -- Working in the cloud -- Introduction to PySpark -- Creating the SparkContext -- Creating an RDD -- Creating a Spark DataFrame -- Summary -- Chapter 3: Finding Patterns in the Noise -- Clustering and Unsupervised Learning -- Similarity and distance metrics -- Numerical distance metrics -- Correlation similarity metrics and time series -- Similarity metrics for categorical data -- K-means clustering -- Affinity propagation -- automatically choosing cluster numbers -- k-medoids -- Agglomerative clustering -- Where agglomerative clustering fails -- Streaming clustering in Spark -- Summary -- Chapter 4: Connecting the Dots with Models -- Regression Methods -- Linear regression -- Data 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| id | ZDB-4-EBU-ocn958874809 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:49:24Z |
| institution | BVB |
| isbn | 9781785889820 1785889826 |
| language | English |
| oclc_num | 958874809 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (1 volume) : illustrations |
| psigel | ZDB-4-EBU |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Packt Publishing, |
| record_format | marc |
| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Babcock, Joseph, author. http://id.loc.gov/authorities/names/n00063574 Mastering predictive analytics with Python : exploit the power of data in your business by building advanced predictive modeling applications with Python / Joseph Babcock. Birmingham, UK : Packt Publishing, [2016]. ©2016 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Community experience distilled Description based on online resource; title from title page (viewed September 19, 2016). Includes index. Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn Gain an insight into components and design decisions for an analytical application Master the use Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover the advanced methods to analyze structured and unstructured data Find out how to deploy a machine learning model in a production environment Visualize the performance of models and the insights they produce Scale your solutions as your data grows using Python Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the ... Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: From Data to Decisions -- Getting Started with Analytic Applications -- Designing an advanced analytic solution -- Data layer: warehouses, lakes, and streams -- Modeling layer -- Deployment layer -- Reporting layer -- Case study: sentiment analysis of social media feeds -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Case study: targeted e-mail campaigns -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Summary -- Chapter 2: Exploratory Data Analysis and Visualization in Python -- Exploring categorical and numerical data in IPython -- Installing IPython notebook -- The notebook interface -- Loading and inspecting data -- Basic manipulations -- grouping, filtering, mapping, and pivoting -- Charting with Matplotlib -- Time series analysis -- Cleaning and converting -- Time series diagnostics -- Joining signals and correlation -- Working with geospatial data -- Loading geospatial data -- Working in the cloud -- Introduction to PySpark -- Creating the SparkContext -- Creating an RDD -- Creating a Spark DataFrame -- Summary -- Chapter 3: Finding Patterns in the Noise -- Clustering and Unsupervised Learning -- Similarity and distance metrics -- Numerical distance metrics -- Correlation similarity metrics and time series -- Similarity metrics for categorical data -- K-means clustering -- Affinity propagation -- automatically choosing cluster numbers -- k-medoids -- Agglomerative clustering -- Where agglomerative clustering fails -- Streaming clustering in Spark -- Summary -- Chapter 4: Connecting the Dots with Models -- Regression Methods -- Linear regression -- Data preparation. Model fitting and evaluation -- Statistical significance of regression outputs -- Generalize estimating equations -- Mixed effects models -- Time series data -- Generalized linear models -- Applying regularization to linear models -- Tree methods -- Decision trees -- Random forest -- Scaling out with PySpark -- predicting year of song release -- Summary -- Chapter 5: Putting Data in its Place -- Classification Methods and Analysis -- Logistic regression -- Multiclass logistic classifiers: multinomial regression -- Formatting a dataset for classification problems -- Learning pointwise updates with stochastic gradient descent -- Jointly optimizing all parameters with second-order methods -- Fitting the model -- Evaluating classification models -- Strategies for improving classification models -- Separating Nonlinear boundaries with Support vector machines -- Fitting and SVM to the census data -- Boosting: combining small models to improve accuracy -- Gradient boosted decision trees -- Comparing classification methods -- Case study: fitting classifier models in pyspark -- Summary -- Chapter 6: Words and Pixels -- Working with Unstructured Data -- Working with textual data -- Cleaning textual data -- Extracting features from textual data -- Using dimensionality reduction to simplify datasets -- Principal component analysis -- Latent Dirichlet Allocation -- Using dimensionality reduction in predictive modeling -- Images -- Cleaning image data -- Thresholding images to highlight objects -- Dimensionality reduction for image analysis -- Case Study: Training a Recommender System in PySpark -- Summary -- Chapter 7: Learning from the Bottom Up -- Deep Networks and Unsupervised Features -- Learning patterns with neural networks -- A network of one -- the perceptron -- Combining perceptrons -- a single-layer neural network -- Parameter fitting with back-propagation. Discriminative versus generative models -- Vanishing gradients and explaining away -- Pretraining belief networks -- Using dropout to regularize networks -- Convolutional networks and rectified units -- Compressing Data with autoencoder networks -- Optimizing the learning rate -- The TensorFlow library and digit recognition -- The MNIST data -- Constructing the network -- Summary -- Chapter 8: Sharing Models with Prediction Services -- The architecture of a prediction service -- Clients and making requests -- The GET requests -- The POST request -- The HEAD request -- The PUT request -- The DELETE request -- Server -- the web traffic controller -- Application -- the engine of the predictive services -- Persisting information with database systems -- Case study -- logistic regression service -- Setting up the database -- The web server -- The web application -- The flow of a prediction service -- training a model -- On-demand and bulk prediction -- Summary -- Chapter 9: Reporting and Testing -- Iterating on Analytic Systems -- Checking the health of models with diagnostics -- Evaluating changes in model performance -- Changes in feature importance -- Changes in unsupervised model performance -- Iterating on models through A/B testing -- Experimental allocation -- assigning customers to experiments -- Deciding a sample size -- Multiple hypothesis testing -- Guidelines for communication -- Translate terms to business values -- Visualizing results -- Case Study: building a reporting service -- The report server -- The report application -- The visualization layer -- Summary -- Index. Business planning Computer programs. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Python (Langage de programmation) BUSINESS & ECONOMICS / Industrial Management bisacsh BUSINESS & ECONOMICS / Management bisacsh BUSINESS & ECONOMICS / Management Science bisacsh BUSINESS & ECONOMICS / Organizational Behavior bisacsh Business planning Computer programs fast Python (Computer program language) fast has work: Mastering predictive analytics with Python (Text) https://id.oclc.org/worldcat/entity/E39PCG9M3d3qr3gqwwxtDCpmv3 https://id.oclc.org/worldcat/ontology/hasWork Print version: 1785882716 9781785882715 (OCoLC)949751072 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1345260 Volltext |
| spellingShingle | Babcock, Joseph Mastering predictive analytics with Python : exploit the power of data in your business by building advanced predictive modeling applications with Python / Community experience distilled. Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: From Data to Decisions -- Getting Started with Analytic Applications -- Designing an advanced analytic solution -- Data layer: warehouses, lakes, and streams -- Modeling layer -- Deployment layer -- Reporting layer -- Case study: sentiment analysis of social media feeds -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Case study: targeted e-mail campaigns -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Summary -- Chapter 2: Exploratory Data Analysis and Visualization in Python -- Exploring categorical and numerical data in IPython -- Installing IPython notebook -- The notebook interface -- Loading and inspecting data -- Basic manipulations -- grouping, filtering, mapping, and pivoting -- Charting with Matplotlib -- Time series analysis -- Cleaning and converting -- Time series diagnostics -- Joining signals and correlation -- Working with geospatial data -- Loading geospatial data -- Working in the cloud -- Introduction to PySpark -- Creating the SparkContext -- Creating an RDD -- Creating a Spark DataFrame -- Summary -- Chapter 3: Finding Patterns in the Noise -- Clustering and Unsupervised Learning -- Similarity and distance metrics -- Numerical distance metrics -- Correlation similarity metrics and time series -- Similarity metrics for categorical data -- K-means clustering -- Affinity propagation -- automatically choosing cluster numbers -- k-medoids -- Agglomerative clustering -- Where agglomerative clustering fails -- Streaming clustering in Spark -- Summary -- Chapter 4: Connecting the Dots with Models -- Regression Methods -- Linear regression -- Data preparation. Model fitting and evaluation -- Statistical significance of regression outputs -- Generalize estimating equations -- Mixed effects models -- Time series data -- Generalized linear models -- Applying regularization to linear models -- Tree methods -- Decision trees -- Random forest -- Scaling out with PySpark -- predicting year of song release -- Summary -- Chapter 5: Putting Data in its Place -- Classification Methods and Analysis -- Logistic regression -- Multiclass logistic classifiers: multinomial regression -- Formatting a dataset for classification problems -- Learning pointwise updates with stochastic gradient descent -- Jointly optimizing all parameters with second-order methods -- Fitting the model -- Evaluating classification models -- Strategies for improving classification models -- Separating Nonlinear boundaries with Support vector machines -- Fitting and SVM to the census data -- Boosting: combining small models to improve accuracy -- Gradient boosted decision trees -- Comparing classification methods -- Case study: fitting classifier models in pyspark -- Summary -- Chapter 6: Words and Pixels -- Working with Unstructured Data -- Working with textual data -- Cleaning textual data -- Extracting features from textual data -- Using dimensionality reduction to simplify datasets -- Principal component analysis -- Latent Dirichlet Allocation -- Using dimensionality reduction in predictive modeling -- Images -- Cleaning image data -- Thresholding images to highlight objects -- Dimensionality reduction for image analysis -- Case Study: Training a Recommender System in PySpark -- Summary -- Chapter 7: Learning from the Bottom Up -- Deep Networks and Unsupervised Features -- Learning patterns with neural networks -- A network of one -- the perceptron -- Combining perceptrons -- a single-layer neural network -- Parameter fitting with back-propagation. Discriminative versus generative models -- Vanishing gradients and explaining away -- Pretraining belief networks -- Using dropout to regularize networks -- Convolutional networks and rectified units -- Compressing Data with autoencoder networks -- Optimizing the learning rate -- The TensorFlow library and digit recognition -- The MNIST data -- Constructing the network -- Summary -- Chapter 8: Sharing Models with Prediction Services -- The architecture of a prediction service -- Clients and making requests -- The GET requests -- The POST request -- The HEAD request -- The PUT request -- The DELETE request -- Server -- the web traffic controller -- Application -- the engine of the predictive services -- Persisting information with database systems -- Case study -- logistic regression service -- Setting up the database -- The web server -- The web application -- The flow of a prediction service -- training a model -- On-demand and bulk prediction -- Summary -- Chapter 9: Reporting and Testing -- Iterating on Analytic Systems -- Checking the health of models with diagnostics -- Evaluating changes in model performance -- Changes in feature importance -- Changes in unsupervised model performance -- Iterating on models through A/B testing -- Experimental allocation -- assigning customers to experiments -- Deciding a sample size -- Multiple hypothesis testing -- Guidelines for communication -- Translate terms to business values -- Visualizing results -- Case Study: building a reporting service -- The report server -- The report application -- The visualization layer -- Summary -- Index. Business planning Computer programs. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Python (Langage de programmation) BUSINESS & ECONOMICS / Industrial Management bisacsh BUSINESS & ECONOMICS / Management bisacsh BUSINESS & ECONOMICS / Management Science bisacsh BUSINESS & ECONOMICS / Organizational Behavior bisacsh Business planning Computer programs fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 |
| title | Mastering predictive analytics with Python : exploit the power of data in your business by building advanced predictive modeling applications with Python / |
| title_auth | Mastering predictive analytics with Python : exploit the power of data in your business by building advanced predictive modeling applications with Python / |
| title_exact_search | Mastering predictive analytics with Python : exploit the power of data in your business by building advanced predictive modeling applications with Python / |
| title_full | Mastering predictive analytics with Python : exploit the power of data in your business by building advanced predictive modeling applications with Python / Joseph Babcock. |
| title_fullStr | Mastering predictive analytics with Python : exploit the power of data in your business by building advanced predictive modeling applications with Python / Joseph Babcock. |
| title_full_unstemmed | Mastering predictive analytics with Python : exploit the power of data in your business by building advanced predictive modeling applications with Python / Joseph Babcock. |
| title_short | Mastering predictive analytics with Python : |
| title_sort | mastering predictive analytics with python exploit the power of data in your business by building advanced predictive modeling applications with python |
| title_sub | exploit the power of data in your business by building advanced predictive modeling applications with Python / |
| topic | Business planning Computer programs. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Python (Langage de programmation) BUSINESS & ECONOMICS / Industrial Management bisacsh BUSINESS & ECONOMICS / Management bisacsh BUSINESS & ECONOMICS / Management Science bisacsh BUSINESS & ECONOMICS / Organizational Behavior bisacsh Business planning Computer programs fast Python (Computer program language) fast |
| topic_facet | Business planning Computer programs. Python (Computer program language) Python (Langage de programmation) BUSINESS & ECONOMICS / Industrial Management BUSINESS & ECONOMICS / Management BUSINESS & ECONOMICS / Management Science BUSINESS & ECONOMICS / Organizational Behavior Business planning Computer programs |
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