Friday, August 26, 2016

Engineering Book: Machine Learning in Python Essential Techniques for Predictive Analysis 1st Edition by Michael Bowles - Review and Free Download

ElectronicKitSchool. - This is Python book edition with titled Machine Learning in Python Essential Techniques for Predictive Analysis 1st Edition that authored by Michael Bowles. 


Machine learning focuses on prediction—using what you know to predict what you would like to know based on historical relationships between the two. At its core, it's a mathematical/algorithm-based technology that, until recently, required a deep understanding of math and statistical concepts, and fluency in R and other specialized languages. Machine Learning in Python simplifies machine learning for a broader audience and wider application by focusing on two algorithm families that effectively predict outcomes, and by showing you how to apply them using the popular and accessible Python programming language.

This article will show you the reviewing of Machine Learning in Python Essential Techniques for Predictive Analysis 1st Edition book like book description and table content. The last in this article we also will give you link to take download e-book of Machine Learning in Python Essential Techniques for Predictive Analysis 1st Edition by Michael Bowles freely.

Overview

From the introduction of this book mentioned that Extracting actionable information from data is changing the fabric of modern business in ways that directly affect programmers. One way is the demand for new programming skills. Market analysts predict demand for people with advanced statistics and machine learning skills will exceed supply by 140,000 to 190,000 by 2018. That means good salaries and a wide choice of interesting projects for those who have the requisite skills. Another development that affects programmers is progress in developing core tools for statistics and machine learning. This relieves programmers of the need to program intricate algorithms for themselves each time they want to try a new one. Among general-purpose programming languages, Python developers have been in the forefront, building state-of-the-art machine learning tools, but there is a gap between having the tools and being able to use them efficiently.

Programmers can gain general knowledge about machine learning in a number of ways: online courses, a number of well-written books, and so on. Many of these give excellent surveys of machine learning algorithms and examples of their use, but because of the availability of so many different algorithms, it’s difficult to cover the details of their usage in a survey.

This leaves a gap for the practitioner. The number of algorithms available requires making choices that a programmer new to machine learning might not be equipped to make until trying several, and it leaves the programmer to fill in the details of the usage of these algorithms in the context of overall problem formulation and solution.

From Amazon also describe that Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.

Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.

  • Predict outcomes using linear and ensemble algorithm families
  • Build predictive models that solve a range of simple and complex problems
  • Apply core machine learning algorithms using PythonUse sample code directly to build custom solutions

Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.

When you read the original book of this Machine Learning in Python Essential Techniques for Predictive Analysis 1st Edition book may be you will see the table content like below.

Table Content

  • Introduction
  • Chapter 1 The Two Essential Algorithms for Making Predictions 
  • Chapter 2 Understand the Problem by Understanding the Data
  • Chapter 3 Predictive Model Building: Balancing Performance,
  • Complexity, and Big Data 
  • Chapter 4 Penalized Linear Regression
  • Chapter 5 Building Predictive Models Using Penalized Linear
  • Methods 
  • Chapter 6 Ensemble Methods
  • Chapter 7 Building Ensemble Models with Python
  • Index 
In the last article we let you to enjoy to get this e-book of Machine Learning in Python Essential Techniques for Predictive Analysis 1st Edition by Michael Bowles with download it freely with the link we given you below.

Download E-book

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Machine Learning in Python Essential Techniques for Predictive Analysis 1st Edition



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