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Rescooped by Alberto Boldt from Plant Biology Teaching Resources (Higher Education)
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Play it Hard - Norman Borlaug 100 Year Tribute - YouTube

The International Center for Maize and Wheat Improvement (CIMMYT) and Biology Fortified have produced a special video tribute to the late Dr. Norman Borlaug,...

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Rescooped by Alberto Boldt from Plant Biology Teaching Resources (Higher Education)
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Science: China's Publication Bazaar (first authorship for sale)

Science: China's Publication Bazaar (first authorship for sale) | Plant Breeding and Machine Learning | Scoop.it

Grim. Science has exposed a thriving academic black market in China involving shady agencies, corrupt scientists, and compromised editors—many of them operating in plain view. The commodity: papers in journals indexed by Thomson Reuters' Science Citation Index, Thomson Reuters' Social Sciences Citation Index, and Elsevier's Engineering Index.


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Conservation concerns for endemic potato species in Bolivia

Conservation concerns for endemic potato species in Bolivia | Plant Breeding and Machine Learning | Scoop.it

A recent study shows that many endemic Bolivian potato species are under threat. Many of these species have tolerance to certain pests, diseases and climate stresses, making them extremely important for future food security.


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Rescooped by Alberto Boldt from Plant Biology Teaching Resources (Higher Education)
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Revised: Teaching Tool on Genetic Improvements in Agriculture

Revised: Teaching Tool on Genetic Improvements in Agriculture | Plant Breeding and Machine Learning | Scoop.it

The Teaching Tools in Plant Biology #16, "Genetic Improvements in Agriculture" has been revised and is available for downloading by Plant Cell subscribes. The revision includes a new section on emerging methods for crop improvement (including cisgenics, transgrafting and CRISPR/Cas), and a new section on GMOs in the news, which describes the response of Rothamsted researchers to a planned field trial destruction, and scientists criticisms of the paper by Seralini et al. (2012).


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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series): Daphne Koller, Nir Friedman: 9780262013192: Amazon.com: Books

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) [Daphne Koller, Nir Friedman] on Amazon.com. *FREE* super saver shipping on qualifying offers.
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Rescooped by Alberto Boldt from BIG data, Data Mining, Predictive Modeling, Visualization
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Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition - Sebans Curve

Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition - Sebans Curve | Plant Breeding and Machine Learning | Scoop.it
Sebans Likes >> Statistical and Machine-Learning Data Mining: Techniques for Better Predic #Management #Leadership http://t.co/z0QjL1EzQC

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7 Steps for Learning Data Mining and Data Science

7 Steps for Learning Data Mining and Data Science | Plant Breeding and Machine Learning | Scoop.it
How to learn data mining and data science? I outline seven steps and point you to resources for becoming a data scientist. (Here are 7 steps for learning data mining and data science.

1. Languages: Learn R, Python, and SQL

2.
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Whats the best model (or) combination of models for credit scoring using data mining techniques?

Whats the best model (or) combination of models for credit scoring using data mining techniques? | Plant Breeding and Machine Learning | Scoop.it
Skip Lahti's answer: Eric, interesting question. What are you using as a "baseline" or comparison to determine accuracy? A FICO score?
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Data Mining and Analysis: Fundamental Concepts and Algorithms ...

New book by Mohammed Zaki and Wagner Meira Jr is a great option for teaching a course in data mining or data science. It covers both fundamental and advanced data mining topics, emphasizing the mathematical foundations and the ...
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Mining Massive Data Sets Graduate Certificate | Stanford University Online

Mining Massive Data Sets Graduate Certificate | Stanford University Online | Plant Breeding and Machine Learning | Scoop.it
Stanford University graduate-level education for working professionals. Master of science degrees, graduate and professional certificates and courses. Delivered online, on campus, and on-site.

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Machine Learning Video Library - Learning From Data (Abu-Mostafa)

Machine Learning Video Library - Learning From Data (Abu-Mostafa) | Plant Breeding and Machine Learning | Scoop.it
Machine Learning Video Library - Learning From Data http://t.co/iA3gArJtR0 via @prismatic
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Top Languages for analytics, data mining, data science

Top Languages for analytics, data mining, data science | Plant Breeding and Machine Learning | Scoop.it
The most popular languages continue to be R (used by 61% of KDnuggets readers), Python (39%), and SQL (37%). SAS is stable at around 20%.
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Data Mining with IBM SPSS Modeler v15

Data Mining with IBM SPSS Modeler v15 | Plant Breeding and Machine Learning | Scoop.it
Having recently completed the course “IBM SPSS Modeler & Data Mining” offered by Global Knowledge, I was looking to find more opportunities to do some modeling with SPSS Modeler. So, when I rea...

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Upcoming Webinar: Lattice Designs - eXtension

Upcoming Webinar: Lattice Designs - eXtension | Plant Breeding and Machine Learning | Scoop.it

This webinar will describe the layout and analysis of lattice field designs.Thursday November 14, 2013 at 1:00 pm EDT. Register now https://www1.gotomeeting.com/register/790644664

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A new approach to mining agricultural gene banks promises to speed the pace of research innovation for food security

A new approach to mining agricultural gene banks promises to speed the pace of research innovation for food security | Plant Breeding and Machine Learning | Scoop.it
Research team for ‘Focused identification of Germplasm Strategy (FIGS)’ opens global consultation to enrich this new tool   

December 4, 2011. An innovative new approach to rapidly ident...

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Machine Learning Tutorial: The Naive Bayes Text Classifier | DatumBox

Machine Learning Tutorial: The Naive Bayes Text Classifier | DatumBox | Plant Breeding and Machine Learning | Scoop.it
In this tutorial we will discuss about Naive Bayes text classifier.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics): Trevor Hastie, Robert Tibshirani, Jerome Friedman: 9780387848570: Amazo...

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) [Trevor Hastie, Robert Tibshirani, Jerome Friedman] on Amazon.com. *FREE* super saver shipping on qualifying offers.
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C++ Developer / Technical Lead - Machine Learning Job in London, London UK

Look at C++ Developer / Technical Lead - Machine Learning jobs in London in London and find all job offers at Client Server Ltd | Monster.co.uk (C++ Developer / Technical Lead - Machine Learning - http://t.co/e7IkjL3CaC
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Rescooped by Alberto Boldt from BIG data, Data Mining, Predictive Modeling, Visualization
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Some Useful Notes on Clustering With R

Some Useful Notes on Clustering With R | Plant Breeding and Machine Learning | Scoop.it

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Data Mining and Business Analytics with R - Free eBook Share

Data Mining and Business Analytics with R - Free eBook Share | Plant Breeding and Machine Learning | Scoop.it
eBook Free Download: Data Mining and Business Analytics with R | PDF, EPUB | ISBN: 111844714X | 2013-05-28 | English | PutLocker

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Fox eBook's curator insight, July 8, 2013 1:40 AM

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:

A thorough discussion and extensive demonstration of the theory behind the most useful data mining toolsIllustrations of how to use the outlined concepts in real-world situationsReadily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materialsNumerous exercises to help readers with computing skills and deepen their understanding of the material

Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Table of Contents

1. Introduction 1
2. Processing the Information and Getting to Know Your Data 7
3. Standard Linear Regression 40
4. Local Polynomial Regression: a Nonparametric Regression Approach 55
5. Importance of Parsimony in Statistical Modeling 67
6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO) 71
7. Logistic Regression 83
8. Binary Classification, Probabilities, and Evaluating Classification Performance 108
9. Classification Using a Nearest Neighbor Analysis 115
10. The Na¨yve Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical
11. Multinomial Logistic Regression 132
12. More on Classification and a Discussion on Discriminant Analysis 150
13. Decision Trees 161
14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods 185
15. Clustering 196
16. Market Basket Analysis: Association Rules and Lift 220
17. Dimension Reduction: Factor Models and Principal Components 235
18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares 247
19. Text as Data: Text Mining and Sentiment Analysis 258
20. Network Data 272

Sundar Rai's curator insight, December 14, 2013 1:32 PM

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