Mastering predictive analytics with python

Mastering Predictive Analytics with Python by Joseph Babcock

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Mastering Predictive Analytics with Python

About This BookMaster open source Python tools to build sophisticated predictive modelsLearn to identify the right machine learning algorithm for your problem with this forward-thinking guideGrasp the major methods of predictive modeling and move beyond the basics to a deeper level of understandingWho 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 LearnGain an insight into components and design decisions for an analytical applicationMaster the use Python notebooks for exploratory data analysis and rapid prototypingGet to grips with applying regression, classification, clustering, and deep learning algorithmsDiscover the advanced methods to analyze structured and unstructured dataFind out how to deploy a machine learning model in a production environmentVisualize the performance of models and the insights they produceScale your solutions as your data grows using PythonEnsure the robustness of your analytic applications by mastering the best practices of predictive analysisIn 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 right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life

This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.

Источник

Mastering Predictive Analytics with Python

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Book description

Exploit the power of data in your business by building advanced predictive modeling applications with Python

• 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

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.

• 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

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 right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life

This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.

Источник

Mastering Predictive Analytics with Python

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 right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life

• 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

Joseph Babcock has spent almost a decade exploring complex datasets and combining predictive modeling with visualization to understand correlations and forecast anticipated outcomes. He received a PhD from the Solomon H. Snyder Department of Neuroscience at The Johns Hopkins University School of Medicine, where he used machine learning to predict adverse cardiac side effects of drugs. Outside the academy, he has tackled big data challenges in the healthcare and entertainment industries.

1. From Data to Decisions – Getting Started with Analytic Applications
2. Exploratory Data Analysis and Visualization in Python
3. Finding Patterns in the Noise – Clustering and Unsupervised Learning
4. Connecting the Dots with Models – Regression Methods
5. Putting Data in its Place – Classification Methods and Analysis
6. Words and Pixels – Working with Unstructured Data
7. Learning from the Bottom Up – Deep Networks and Unsupervised Features
8. Sharing Models with Prediction Services
9. Reporting and Testing – Iterating on Analytic Systems

Источник