Kamis, 13 Desember 2012

PDF Download Theory of Spectrochemical ExcitationBy Paul W. Boumans

PDF Download Theory of Spectrochemical ExcitationBy Paul W. Boumans

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Theory of Spectrochemical ExcitationBy Paul W. Boumans

Theory of Spectrochemical ExcitationBy Paul W. Boumans


Theory of Spectrochemical ExcitationBy Paul W. Boumans


PDF Download Theory of Spectrochemical ExcitationBy Paul W. Boumans

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Theory of Spectrochemical ExcitationBy Paul W. Boumans

There are more things in heaven and earth, Horatio, Than are dreamt of in your philosophy. Hamlet There exists a fairly large number of textbooks concerned with spectrochemical analysis. Most of them deal with practical applications and instrumental factors, and provide the reader with the knowledge indispensable for conducting analyses with the help of emission spectra. Practical knowledge and experience are indeed important requisites for success­ fully exploiting the spectrochemical method in the field of analytical chemistry. As the method is essentially empirical, it is, in principle, a simple one, provided that we succeed in exciting all samples in an identical manner; for then, relative intensities of spectral lines can serve as the 'weights' by which to measure amounts of elements. However, creating the required constancy of excitation conditions is hampered by the very nature of the sample, whose composition profoundly influences the excitation characteristics of the light source. Therefore, spectrochemists are inevitably engaged in all the processes that determine the radiation output of the light source for a given sample. Dealing, with this ensemble of processes, that is, with 'excitation' in the widest sense, is the object of this book (cf. § 1. 1). The reader will seek in vain for enumerations of practical rules that would tell him how to tackle a particular analysis problem.

  • Sales Rank: #10865659 in Books
  • Published on: 2013-10-04
  • Released on: 2013-10-04
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.61" h x .91" w x 6.69" l, 1.40 pounds
  • Binding: Paperback
  • 384 pages

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Jumat, 07 Desember 2012

Download Ebook Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs

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Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs

Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs


Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs


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Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs

Key Features

  • Put machine learning principles into practice to solve real-world problems
  • Get to grips with Python's impressive range of Machine Learning libraries and frameworks
  • From retrieving data from APIs to cleaning and visualization, become more confident at tackling every stage of the data pipeline
Book Description

Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it?

Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice.

You’ll learn how to use cluster techniques to discover bargain air fares, and apply linear regression to find yourself a cheap apartment – and much more. Everything you learn is backed by a real-world example, whether its data manipulation or statistical modelling.

That way you’re never left floundering in theory – you’ll be simply collecting and analyzing data in a way that makes a real impact.

What you will learn
  • Explore and use Python's impressive machine learning ecosystem
  • Successfully evaluate and apply the most effective models to problems
  • Learn the fundamentals of NLP - and put them into practice
  • Visualize data for maximum impact and clarity
  • Deploy machine learning models using third party APIs
  • Get to grips with feature engineering
About the Author

Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.

Table of Contents
  • The Python Machine Learning Ecosystem
  • Build an App to Find Underpriced Apartments
  • Build an App to Find Cheap Airfares
  • Forecast the IPO Market using Logistic Regression
  • Create a Custom Newsfeed
  • Predict whether Your Content Will Go Viral
  • Forecast the Stock Market with Machine Learning
  • Build an Image Similarity Engine
  • Build a Chatbot
  • Build a Recommendation Engine
    • Amazon Sales Rank: #350665 in Books
    • Published on: 2016-07-29
    • Released on: 2016-07-29
    • Original language: English
    • Dimensions: 9.25" h x .75" w x 7.50" l, 1.26 pounds
    • Binding: Paperback
    • 332 pages

    About the Author Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a fulltime lead instructor for a data science immersive program in New York City.

    Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs PDF
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    Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs PDF

    Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs PDF

    Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs PDF
    Python Machine Learning Blueprints: Intuitive data projects you can relate toBy Alexander T. Combs PDF