Mastering Predictive Analytics with Python

Babcock, Joseph

ISBN 10: 1785882716 / ISBN 13: 9781785882715

Edité par Packt Publishing, 2016

A propos de cet article

&Uumlber den AutorJoseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale c. N° de réf. du vendeur 148485565

Détails bibliographiques

Titre : Mastering Predictive Analytics with Python

Éditeur : Packt Publishing

Date d'édition : 2016

Reliure : Couverture souple

Etat : New

A propos de ce titre

Synopsis :

Key Features

• 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

Book Description

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

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

About the Author

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.

Table of Contents

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

À propos de l?auteur:

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.