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Ensemble Learning for AI Developers - by  Alok Kumar & Mayank Jain (Paperback) - 1 of 1

Ensemble Learning for AI Developers - by Alok Kumar & Mayank Jain (Paperback)

$49.99

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About this item

Highlights

  • Use ensemble learning techniques and models to improve your machine learning results.Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed.
  • About the Author: Alok Kumar is an AI practitioner and innovation lead at Publicis Sapient.
  • 136 Pages
  • Computers + Internet, Artificial Intelligence

Description



Book Synopsis



Use ensemble learning techniques and models to improve your machine learning results.
Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook.

What You Will Learn

  • Understand the techniques and methods utilized in ensemble learning
  • Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias
  • Enhance your machine learning architecture with ensemble learning


Who This Book Is For

Data scientists and machine learning engineers keen on exploring ensemble learning



From the Back Cover



Use ensemble learning techniques and models to improve your machine learning results.
Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook.
You will:

  • Understand the techniques and methods utilized in ensemble learning
  • Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias
  • Enhance your machine learning architecture with ensemble learning




About the Author



Alok Kumar is an AI practitioner and innovation lead at Publicis Sapient. He has extensiveexperience in leading strategic initiatives and driving cutting-edge, fast-paced innovations. He won several awards and he is passionate about democratizing AI knowledge. He manages multiple non- profit learning and creative groups in NCR.


Mayank Jain currently works as Manager Technology at the Publicis Sapient Innovation Lab Kepler as an AI/ML expert. He has more than 10 years of industry experience working on cutting-edge projects to make computers see and think using techniques such as deep learning, machine learning, and computer vision. He has written several international publications, holds patents in his name, and has been awarded multiple times for his contributions.
Dimensions (Overall): 9.21 Inches (H) x 6.14 Inches (W) x .33 Inches (D)
Weight: .5 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 136
Genre: Computers + Internet
Sub-Genre: Artificial Intelligence
Publisher: Apress
Theme: General
Format: Paperback
Author: Alok Kumar & Mayank Jain
Language: English
Street Date: June 19, 2020
TCIN: 1011990064
UPC: 9781484259399
Item Number (DPCI): 247-19-4114
Origin: Made in the USA or Imported
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Shipping details

Estimated ship dimensions: 0.33 inches length x 6.14 inches width x 9.21 inches height
Estimated ship weight: 0.5 pounds
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Q: What is the target audience for this book?

submitted by AI Shopping Assistant - 23 hours ago
  • A: The book is aimed at data scientists and machine learning engineers interested in exploring ensemble learning techniques.

    submitted byAI Shopping Assistant - 23 hours ago
    Ai generated

Q: What are the key techniques covered in this book?

submitted by AI Shopping Assistant - 23 hours ago
  • A: The book covers techniques like bagging, stacking, boosting, and random forest models to enhance machine learning performance.

    submitted byAI Shopping Assistant - 23 hours ago
    Ai generated

Q: Who are the authors of this book?

submitted by AI Shopping Assistant - 23 hours ago
  • A: The authors are Alok Kumar, an AI practitioner, and Mayank Jain, a Manager Technology at Publicis Sapient.

    submitted byAI Shopping Assistant - 23 hours ago
    Ai generated

Q: What programming libraries are discussed in the book?

submitted by AI Shopping Assistant - 23 hours ago
  • A: The book discusses popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM.

    submitted byAI Shopping Assistant - 23 hours ago
    Ai generated

Q: What type of data science problems does the book address?

submitted by AI Shopping Assistant - 23 hours ago
  • A: It addresses various problems including time series data, imaging data, and natural language processing (NLP).

    submitted byAI Shopping Assistant - 23 hours ago
    Ai generated

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