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Mastering Predictive Analytics with Python
Mastering Predictive Analytics with Python

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

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Profile Icon Joseph Babcock
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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (2 Ratings)
Paperback Aug 2016 334 pages 1st Edition
eBook
Can$53.09 Can$58.99
Paperback
Can$73.99
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Free Trial
Arrow left icon
Profile Icon Joseph Babcock
Arrow right icon
Free Trial
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (2 Ratings)
Paperback Aug 2016 334 pages 1st Edition
eBook
Can$53.09 Can$58.99
Paperback
Can$73.99
Subscription
Free Trial
eBook
Can$53.09 Can$58.99
Paperback
Can$73.99
Subscription
Free Trial

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Table of content icon View table of contents Preview book icon Preview Book

Mastering Predictive Analytics with Python

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Key benefits

  • 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

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

Who is this book 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 31, 2016
Length: 334 pages
Edition : 1st
Language : English
ISBN-13 : 9781785882715
Category :
Languages :

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Product Details

Publication date : Aug 31, 2016
Length: 334 pages
Edition : 1st
Language : English
ISBN-13 : 9781785882715
Category :
Languages :

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Table of Contents

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

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 0%
1 star 50%
Amazon Customer Oct 05, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Walks through cross-section of different modeling problems and full implementation of scalable service on pyspark; lots of code examples and practical advice. Equation formatting could use some work.
Amazon Verified review Amazon
AMGAustin Jun 23, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Very frustrating book to try to learn from. You often have to go searching for the data because it is not included in what can be downloaded and the book provides no clear instructions about how to get it. The author also has a habit of providing instructions for how to run a model, but then gives no discussion about what the point is. I would not recommend this book.
Amazon Verified review Amazon
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