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Learning Python by Mark Lutz

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1[LinkedInx Learning] - Técnicas Avançadas De Python

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The book is available for download in "data" format, the size of the file-s is: 1388.37 Mbs, the file-s for this book were downloaded 12 times, the file-s went public at Thu Nov 11 2021.

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2Learning Python

Learn Python from start to end

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  • Title: Learning Python
  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 541.00 Mbs, the file-s for this book were downloaded 55 times, the file-s went public at Sun Dec 17 2023.

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3Learning Python. Network. Programming( 2015, Sarker, Washington; Packt)

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Welcome to the world of network programming with Python. Python is a full-featured object-oriented programming language with a standard library that includes everything needed to rapidly build powerful network applications. In addition, it has a multitude of third-party libraries and packages that extend Python to every sphere of network programming. Combined with the fun of using Python, with this book, we hope to get you started on your journey so that you master these tools and produce some great networking code. In this book, we are squarely targeting Python 3. Although Python 3 is still establishing itself as the successor to Python 2, version 3 is the future of the language, and we want to demonstrate that it is ready for network programming prime time. It offers many improvements over the previous version, many of which improve the network programming experience, with enhanced standard library modules and new additions. We hope you enjoy this introduction to network programming with Python. Dr. M. O. Faruque Sarker    Sam Washington

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The book is available for download in "texts" format, the size of the file-s is: 127.20 Mbs, the file-s for this book were downloaded 280 times, the file-s went public at Mon Aug 03 2020.

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4Imbalanced-learn: A Python Toolbox To Tackle The Curse Of Imbalanced Datasets In Machine Learning

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Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub: https://github.com/scikit-learn-contrib/imbalanced-learn.

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The book is available for download in "texts" format, the size of the file-s is: 0.13 Mbs, the file-s for this book were downloaded 43 times, the file-s went public at Fri Jun 29 2018.

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5Mlpy: Machine Learning Python

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mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3 at the website http://mlpy.fbk.eu.

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The book is available for download in "texts" format, the size of the file-s is: 2.67 Mbs, the file-s for this book were downloaded 983 times, the file-s went public at Mon Sep 23 2013.

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6Deep Learning With Python : A Hands-on Introduction

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mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3 at the website http://mlpy.fbk.eu.

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The book is available for download in "texts" format, the size of the file-s is: 332.31 Mbs, the file-s for this book were downloaded 156 times, the file-s went public at Fri Dec 15 2023.

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7Thursday - 203 - 5 - Python For Brain Mining: (Neuro)science With State Of The Art Machine Learning And Data Visualization

Python for Brain Mining: (Neuro)science with State of the Art Machine Learning and Data Visualization Gael Varoquaux If you have questions, email [email protected]

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The book is available for download in "movies" format, the size of the file-s is: 530.91 Mbs, the file-s for this book were downloaded 369 times, the file-s went public at Sat Jul 30 2011.

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8Practical Machine Learning By Example In Python

Practical Machine Learning By Example In Python

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9Deep Learning De A A La Z - Redes Neuronales En Python Desde Cero (2020)(Udemy)

Deep Learning de A a la  Z - Redes Neuronales en Python desde Cero (2020)(Udemy)

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The book is available for download in "movies" format, the size of the file-s is: 15096.79 Mbs, the file-s for this book were downloaded 634 times, the file-s went public at Tue Jun 22 2021.

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10Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (18 - Support Vector Machine (SVM))

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (18 - Support Vector Machine (SVM))

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The book is available for download in "movies" format, the size of the file-s is: 78.53 Mbs, the file-s for this book were downloaded 45 times, the file-s went public at Sat Feb 10 2024.

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11Andrew Park Data Science For Beginners 4 Books In 1 Python Programming, Data Analysis, Machine Learning. A Complete Overview To Master The Art Of Data Science From Scratch Using Python For Busines

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random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

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The book is available for download in "texts" format, the size of the file-s is: 229.00 Mbs, the file-s for this book were downloaded 1304 times, the file-s went public at Wed May 08 2024.

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12Learning_Python_-_O_Reilly_4th_Edition

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

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13Learning Python

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Includes index

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The book is available for download in "texts" format, the size of the file-s is: 407.65 Mbs, the file-s for this book were downloaded 3938 times, the file-s went public at Tue Jun 15 2010.

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14Introduction To Machine Learning With Python ( PDFDrive.com )

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My library

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The book is available for download in "texts" format, the size of the file-s is: 7089.26 Mbs, the file-s for this book were downloaded 5179 times, the file-s went public at Wed Feb 03 2021.

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15Introduction To Machine Learning With Python ( PDFDrive.com )

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books

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16An Introduction To Statistical Learning With Applications In Python

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

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The book is available for download in "texts" format, the size of the file-s is: 326.80 Mbs, the file-s for this book were downloaded 16 times, the file-s went public at Thu Aug 01 2024.

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17Learning OpenCV 3 Computer Vision With Python : Unleash The Power Of Computer Vision With Python Using OpenCV

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As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

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The book is available for download in "texts" format, the size of the file-s is: 627.64 Mbs, the file-s for this book were downloaded 74 times, the file-s went public at Fri Jun 09 2023.

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18มองโลก มองไทย - 'Python' ภาษาเบื้องต้นสำหรับ Machine Learning

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#มองโลกมองไทย ประจำวันที่ 25 ตุลาคม 2563 คุยกับคอมพิวเตอร์ด้วยภาษามนุษย์? ปราบ เลาหะโรจนพันธ์ อาจารย์พิเศษ วิชา Machine Learning ม.ธรรมศาสตร์ แขกรับเชิญพิเศษจะมาเล่าให้ฟังว่า 'ทักษะภาษาคอมพ์ฯ' ช่วยให้เราพลิกวิธีคิด พลิกอนาคตอย่างไร และทำไม โลกถึงนิยม และมองหาคนที่มีทักษะนี้เสียเหลือเกิน? Machine Learning สำหรับมนุษย์เงินเดือน โดย ThinkLab Creative Space & Cafe https://www.facebook.com/thinklab.creativespace/posts/194751758721175 พิเศษ! เพียงระบุว่ารับชมมาจากรายการ "มองโลก มองไทย" รับส่วนลด 10% Facebook Page: ThinkLab Creative Space and Cafe โทรศัพท์/ LINE ID: 0610247199 สมัครสมาชิกเพื่อรับสิทธิพิเศษ (Membership) https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw/join ติดตาม #VoiceTV YouTube : https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw Facebook : https://www.facebook.com/pg/VoiceTVRankingThailand Instagram : https://www.instagram.com/voicetv/ Twitter : https://twitter.com/VoiceTVOfficial Website : https://www.voicetv.co.th/ Source: https://www.youtube.com/watch?v=fUrtterb4Z0 Uploader: VOICE TV

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19Persian Python Learning

learning programming with python in persian

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  • Language: per

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The book is available for download in "movies" format, the size of the file-s is: 2446.42 Mbs, the file-s for this book were downloaded 429 times, the file-s went public at Sat Aug 04 2018.

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20Scikit-learn: Machine Learning In Python

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learning programming with python in persian

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21Python Environment For Bayesian Learning: Inferring The Structure Of Bayesian Networks From Knowledge And Data(Machine Learning Open Source Software Paper)

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learning programming with python in persian

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The book is available for download in "data" format, the size of the file-s is: 0.02 Mbs, the file-s for this book were downloaded 35 times, the file-s went public at Tue Aug 11 2020.

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22Sentimental Emotion Analysis Using Python And Machine Learning

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Sentiment analysis is used in opinion mining. It helps businesses understand the customers’ reviews with a particular product by analyzing their emotional from the product reviews they post, the online recommendations they make, their survey responses and other forms of social media text. Businesses can get feedback on how happy or sad the customer is, and use this insight to gain a competitive edge. In this article, we explore how to conduct sentiment analysis on a piece of text using some machine learning techniques. Python happens to be one of the best programming language, when it comes to machine learning as it is easy to learn, is open source, and is effective in catering to machine learning requirements like processing big datasets and performing mathematical computations. Natural Language ToolKit NLTK is one of the popular packages in Python that can use for in sentiment analysis. Mohit Chaudhari "Sentimental Emotion Analysis using Python and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41198.pdf Paper URL: https://www.ijtsrd.comengineering/computer-engineering/41198/sentimental-emotion-analysis-using-python-and-machine-learning/mohit-chaudhari

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The book is available for download in "texts" format, the size of the file-s is: 5.31 Mbs, the file-s for this book were downloaded 56 times, the file-s went public at Sat Jul 10 2021.

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23Solar Wind In Situ Data Suitable For Machine Learning (python Numpy Structured Arrays): STEREO-A/B, Wind, Parker Solar Probe, Ulysses, Venus Express, MESSENGER

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These are solar wind in situ data arrays in python pickle format suitable for machine learning, i.e. the arrays consist only of numbers, no strings and no datetime objects. See AAREADME_insitu_ML.txt for more explanation. If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin): [email protected] or twitter @chrisoutofspace Made with https://github.com/cmoestl/heliocats Load in python with e.g. for Parker Solar Probe data: > import pickle > filepsp='psp_2018_2019_sceq_ndarray.p' > [psp,hpsp]=pickle.load(open(filepsp, "rb" ) ) plot time vs total field > import matplotlib.pyplot as plt > plt.plot(psp['time'],psp['bt']) Times psp[:,0 ] or psp['time'] are in matplotlib format. Variable 'hpsp' contains a header with the variable names and units for each column. Coordinate systems for magnetic field components are RTN (Ulysses), SCEQ (Parker Solar Probe, STEREO-A/B, VEX, MESSENGER), HEEQ (Wind) available parameters: bt = total magnetic field bxyz = magnetic field components vt = total proton speed vxyz = velocity components (only for PSP) np = proton density tp = proton temperature xyz = spacecraft position in HEEQ r, lat, lon = spherical coordinates of position in HEEQ

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24Free Course Site.com Udemy Machine Learning A Z™ Python & R In Data Science [ 2023]

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25Github.com-rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49

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The "Python Machine Learning (2nd edition)" book code repository and info resource Python Machine Learning (2nd Ed.) Code Repository Python Machine Learning, 2nd Ed. published September 20th, 2017 Paperback: 622 pages Publisher: Packt Publishing Language: English ISBN-10: 1787125939 ISBN-13: 978-1787125933 Kindle ASIN: B0742K7HYF Links Amazon Page Packt Page Table of Contents and Code Notebooks Helpful installation and setup instructions can be found in the README.md file of Chapter 1 To access the code materials for a given chapter, simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub. In addition, the code/ subdirectories also contain .py script files, which were created from the Jupyter Notebooks. However, I highly recommend working with the Jupyter notebook if possible in your computing environment. Not only do the Jupyter notebooks contain the images and section headings for easier navigation, but they also allow for a stepwise execution of individual code snippets, which -- in my opinion -- provide a better learning experience. Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text. Machine Learning - Giving Computers the Ability to Learn from Data [[open dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] Training Machine Learning Algorithms for Classification [[open dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] A Tour of Machine Learning Classifiers Using Scikit-Learn [[open dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] Building Good Training Sets – Data Pre-Processing [[open dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] Compressing Data via Dimensionality Reduction [[open dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[open dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] Combining Different Models for Ensemble Learning [[open dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] Applying Machine Learning to Sentiment Analysis [[open dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] Embedding a Machine Learning Model into a Web Application [[open dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] Predicting Continuous Target Variables with Regression Analysis [[open dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] Working with Unlabeled Data – Clustering Analysis [[open dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] Implementing a Multi-layer Artificial Neural Network from Scratch [[open dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] Parallelizing Neural Network Training with TensorFlow [[open dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] Going Deeper: The Mechanics of TensorFlow [[open dir](./code/ch14)] [[ipynb](./code/ch14/ch14.ipynb)] Classifying Images with Deep Convolutional Neural Networks [[open dir](./code/ch15)] [[ipynb](./code/ch15/ch15.ipynb)] Modeling Sequential Data Using Recurrent Neural Networks [[open dir](./code/ch16)] [[ipynb](./code/ch16/ch16.ipynb)] What’s new in the second edition from the first edition? Oh, there are so many things that we improved or added; where should I start!? The one issue on top of my priority list was to fix all the nasty typos that were introduced during the layout stage or my oversight. I really appreciated all the helpful feedback from readers in this manner! Furthermore, I addressed all the feedback about sections that may have been confusing or a bit unclear, reworded paragraphs, and added additional explanations. Also, special thanks go to the excellent editors of the second edition, who helped a lot along the way! Also, the figures and plots became much prettier. While readers liked the graphic content a lot, some people criticized the PowerPoint-esque style and layout. Thus, I decided to overhaul every little figure with a hopefully more pleasing choice of fonts and colors. Also, the data plots look much nicer now, thanks to the matplotlib team who put a lot of work in matplotlib 2.0 and its new styling theme. Beyond all these cosmetic fixes, new sections were added here and there. Among these is, for example, is a section on dealing with imbalanced datasets, which several readers were missing in the first edition and short section on Latent Dirichlet Allocation among others. As time and the software world moved on after the first edition was released in September 2015, we decided to replace the introduction to deep learning via Theano. No worries, we didn't remove it but it got a substantial overhaul and is now based on TensorFlow, which has become a major player in my research toolbox since its open source release by Google in November 2015. Along with the new introduction to deep learning using TensorFlow, the biggest additions to this new edition are three brand new chapters focussing on deep learning applications: A more detailed overview of the TensorFlow mechanics, an introduction to convolutional neural networks for image classification, and an introduction to recurrent neural networks for natural language processing. Of course, and in a similar vein as the rest of the book, these new chapters do not only provide readers with practical instructions and examples but also introduce the fundamental mathematics behind those concepts, which are an essential building block for understanding how deep learning works. [ [Excerpt from "Machine Learning can be useful in almost every problem domain:" An interview with Sebastian Raschka](https://www.packtpub.com/books/content/machine-learning-useful-every-problem-domain-interview-sebastian-raschka/) ] Raschka, Sebastian, and Vahid Mirjalili. Python Machine Learning, 2nd Ed . Packt Publishing, 2017. @book{RaschkaMirjalili2017, address = {Birmingham, UK}, author = {Raschka, Sebastian and Mirjalili, Vahid}, edition = {2}, isbn = {978-1787125933}, keywords = {Clustering,Data Science,Deep Learning, Machine Learning,Neural Networks,Programming, Supervised Learning}, publisher = {Packt Publishing}, title = {{Python Machine Learning, 2nd Ed.}}, year = {2017} } Translations German ISBN-10: 3958457339 ISBN-13: 978-3958457331 Amazon.de link Publisher link Japanese ISBN-10: 4295003379 ISBN-13: 978-4295003373 Amazon.co.jp link To restore the repository download the bundle wget https://archive.org/download/github.com-rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49/rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49.bundle and run: git clone rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49.bundle Source: https://github.com/rasbt/python-machine-learning-book-2nd-edition Uploader: rasbt Upload date: 2019-11-15

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26Deployment Of Machine Learning Models In Production Python

Deployment of Machine Learning Models in Production Python

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27Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (30 - Eclat)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (30 - Eclat)

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28Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (46 - Annex Logistic Regression (Long Explanation))

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (46 - Annex Logistic Regression (Long Explanation))

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29Andreas C. Müller, Sarah Guido Introduction To Machine Learning With Python A Guide For Data Scientists O’ Reilly Media ( 2016) 3 ( 1)

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Introduction-to-machine-learning-with-python-a-guide for data scientists by andreas-c.-muller-sarah-guido

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30Andrew Park Data Science For Beginners 4 Books In 1 Python Programming, Data Analysis, Machine Learning. A Complete Overview To Master The Art Of Data Science From Scratch Using Python For Busines

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random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

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31Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (45 - Exclusive Offer)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (45 - Exclusive Offer)

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32Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (09 - Support Vector Regression (SVR))

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33Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (10 - Decision Tree Regression)

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34Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (17 - K-Nearest Neighbors (K-NN))

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35Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (19 - Kernel SVM)

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36Introduction To Machine Learning With Python

Power of Machine Learning with python

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37Introduction To Machine Learning With Python : A Guide For Data Scientists

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38NASA Technical Reports Server (NTRS) 20220003102: MLtool: Universal Supervised Machine Learning Tool To Model Tabulated Data MLtool Python Code

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Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

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39How To Think Like A Computer Scientist Learning Python

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How To Think Like A Computer Scientist Learning Python

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40GitHub - What Are You Focused On Learning Right Now? From Rust And Python To Italian, Here's What Our Community Of Maintainers Are Educating Themselves On. Read More Of Their Stories Here:

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What are you focused on learning right now? From Rust and Python to Italian, here's what our community of maintainers are educating themselves on. Read more of their stories here: https://t.co/0fmuxQyYM2 https://t.co/VwfH6YUjSX Source: https://twitter.com/github/status/1555626135781875712 Uploader: GitHub

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41[EuroPython 2016] Ian Lewis - Deep Learning With Python & TensorFlow

Ian Lewis - Deep Learning with Python & TensorFlow [EuroPython 2016] [22 July 2016 / 2016-07-22] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/deep-learning-with-python-tensorflow) Python has lots of scientific, data analysis, and machine learning libraries. But there are many problems when starting out on a machine learning project. Which library do you use? How do they compare to each other? How can you use a model that has been trained in your production app? In this talk I will discuss how you can use TensorFlow to create Deep Learning applications. I will discuss how it compares to other Python machine learning libraries, and how to deploy into production. ----- Python has lots of scientific, data analysis, and machine learning libraries. But there are many problems when starting out on a machine learning project. Which library do you use? How do they compare to each other? How can you use a model that has been trained in your production application? TensorFlow is a new Open-Source framework created at Google for building Deep Learning applications. Tensorflow allows you to construct easy to understand data flow graphs in Python which form a mathematical and logical pipeline. Creating data flow graphs allow easier visualization of complicated algorithms as well as running the training operations over multiple hardware GPUs in parallel. In this talk I will discuss how you can use TensorFlow to create Deep Learning applications. I will discuss how it compares to other Python machine learning libraries like Theano or Chainer. Finally, I will discuss how trained TensorFlow models could be deployed into a production system using TensorFlow Serve.

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42Learning Python

This is a beginner level book.

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43Python: Learn Python In 24 Hours Or Less - A Beginner’s Guide To Learning Python Programming Now

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This is a beginner level book.

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44ERIC ED590389: 2018 Brick & Click: An Academic Library Conference (18th, Maryville, Missouri, November 2, 2018) Sixteen Scholarly Papers And Twenty Abstracts Comprise The Content Of The Eighteenth Annual Brick & Click Libraries Conference, Held Annually At Northwest Missouri State University In Maryville, Missouri. The Proceedings, Authored By Academic Librarians And Presented At The Conference, Portray The Contemporary And Future Face Of Librarianship. The 2018 Paper And Abstract Titles Include: (1) Committee On Diversity & Inclusion: Cultivating An Inclusive Library Environment (Orolando Duffus, Andrea Malone, Margaret Dunn, Lisa Cruces, Matthew Moore, Annie Wu, And Frederick Young); (2) Checking Out The LGBT+ (Kayla Reed); (3) Tailoring Library Instruction To Adult Students: Applying The Science And Methods Of Andragogy For Modern Instructional And Reference Services (Eric Deatherage And Jason Smith); (4) Library-Faculty Collaboration For OER Promotion And Implementation (Paula Martin); (5) The Facts Of Fiction: Research For Creative Writers (Addison Lucchi); (6) Location And The Collection Connection (Kayla Reed And Amber Carr); (7) Gay For No Pay: How To Maintain An LGBTQ+ Collection With No Budget (Rachel Wexelbaum); (8) A Step Up: Piloting Integrated Information Literacy Instruction Throughout A Discipline (Nathan Elwood And Robyn Hartman); (9) Not Just A Collection: The Emergence And Evolution Of Our Contemporary Collection (Hong Li And Kayla Reed); (10) Flipster: How One Community College Library Supports Faculty And Student Academic Needs With Flipster Digital Magazines (Stephen Ambra); (11) Three Ring Circus: A Model For Understanding And Teaching Students About Bias (Virginia Cairns); (12) Demystifying DH: How To Get Started With Digital Humanities (Sherri Brown And Forstot Burke); (13) Academic Libraries Embracing Technology With A Purpose (Lavoris Martin); (14) (A)ffective Management: A People First Management Approach (Ryan Weir); (15) Plugged & Unplugged Active Learning Strategies For One Shots (Judy Bastin, Justina Mollach, Leslie Pierson, Ruth Harries, And Teresa Mayginnes); (16) Giving A Booster Shot To Your One Shot: Incorporating Engaging Activities Into Library Instruction (Kelly Leahy, Gwen Wilson, And Angela Beatie); (17) Adventures With Omeka.net: Metadata, Workflows, And Exhibit-based Storytelling At UNO Libraries (Yumi Ohira, Angela Kroeger, And Lori Schwartz); (18) Online Badge Classes For High School Students (Angela Paul); (19) Fake News: The Fun, The Fear, And The Future Of Resource Evaluation (Lindsay Brownfield); (20) Making Outreach The Library's Mission (April K. Miller); (21) Active Learning For Metaliteracies: Digital Modules From The New Literacies Alliance (Rachel R. Vukas, Prasanna Vaduvathiriyan, And Brenda Linares); (22) Calculating Return On Investment In Libraries (Nicholas Wyant); (23) Crossing Borders: Expanding Digitization Efforts Across Library Departments (Jay Trask, Jane Monson, And Jessica Hayden); (24) From Silos To Collaboration (Joyce Meldrem); (25) Key Performance Indicator Tracking Using Google Forms (Joshua Lambert); (26) Bridging The Gap: Providing Equal Access Of Library Resources And Services To Distance Learners (Nancy Crabtree, Xiaocan (Lucy) Wang, Bob Black); (27) Coming To The Plains: Latino/a Stories In Nebraska (Laurinda Weisse, Michelle Warren, And Jacob Rosdail); (28) Five Keys To #SocialMediaSuccess In Academic Libraries (Hannah E. Christian And Alison Hanner); (29) Easy Information Literacy Assessments For Small Academic Libraries (Julie Pinnell); (30) Traversing The Path: A Library Director's Guide To The Higher Learning Commission's Open Pathway For Accreditation (Sandy Moore); (31) Drawing Magic: Visualizing The Internet To Introduce Information Literacy (Kelly Leahy); (32) Chatspeak For Librarians: Best Practices For Chat Reference (Tanner D. Lewey); (33) The Creative Learning Spiral: A Python Learner In The Library (Greta Valentine); (34) The Poet's Papers: Literary Research In The Small College Archives (Martha A. Tanner); (35) Giving Students An Edge: Enhancing Resumes With A Digital Information Research Certificate (Rachel R. Vukas); And (36) Where Did You Get That EBook? Comparison Of Student/Faculty Use Of EBooks, Library Space, And Citation Management Programs (Alice B. Ruleman). (Individual Papers Contain References.) [For The 2017 Proceedings, See ED578189.]

By

Sixteen scholarly papers and twenty abstracts comprise the content of the eighteenth annual Brick & Click Libraries Conference, held annually at Northwest Missouri State University in Maryville, Missouri. The proceedings, authored by academic librarians and presented at the conference, portray the contemporary and future face of librarianship. The 2018 paper and abstract titles include: (1) Committee on Diversity & Inclusion: Cultivating an Inclusive Library Environment (Orolando Duffus, Andrea Malone, Margaret Dunn, Lisa Cruces, Matthew Moore, Annie Wu, and Frederick Young); (2) Checking Out the LGBT+ (Kayla Reed); (3) Tailoring Library Instruction to Adult Students: Applying the Science and Methods of Andragogy for Modern Instructional and Reference Services (Eric Deatherage and Jason Smith); (4) Library-Faculty Collaboration for OER Promotion and Implementation (Paula Martin); (5) The Facts of Fiction: Research for Creative Writers (Addison Lucchi); (6) Location and the Collection Connection (Kayla Reed and Amber Carr); (7) Gay for No Pay: How to Maintain an LGBTQ+ Collection with No Budget (Rachel Wexelbaum); (8) A Step Up: Piloting Integrated Information Literacy Instruction Throughout a Discipline (Nathan Elwood and Robyn Hartman); (9) Not Just a Collection: The Emergence and Evolution of Our Contemporary Collection (Hong Li and Kayla Reed); (10) Flipster: How One Community College Library Supports Faculty and Student Academic Needs with Flipster Digital Magazines (Stephen Ambra); (11) Three Ring Circus: A Model for Understanding and Teaching Students about Bias (Virginia Cairns); (12) Demystifying DH: How to Get Started with Digital Humanities (Sherri Brown and Forstot Burke); (13) Academic Libraries Embracing Technology with a Purpose (Lavoris Martin); (14) (A)ffective Management: A People First Management Approach (Ryan Weir); (15) Plugged & Unplugged Active Learning Strategies for One Shots (Judy Bastin, Justina Mollach, Leslie Pierson, Ruth Harries, and Teresa Mayginnes); (16) Giving a Booster Shot to Your One Shot: Incorporating Engaging Activities into Library Instruction (Kelly Leahy, Gwen Wilson, and Angela Beatie); (17) Adventures with Omeka.net: Metadata, Workflows, and Exhibit-based Storytelling at UNO Libraries (Yumi Ohira, Angela Kroeger, and Lori Schwartz); (18) Online Badge Classes for High School Students (Angela Paul); (19) Fake News: The Fun, the Fear, and the Future of Resource Evaluation (Lindsay Brownfield); (20) Making Outreach the Library's Mission (April K. Miller); (21) Active Learning for Metaliteracies: Digital Modules from the New Literacies Alliance (Rachel R. Vukas, Prasanna Vaduvathiriyan, and Brenda Linares); (22) Calculating Return on Investment in Libraries (Nicholas Wyant); (23) Crossing Borders: Expanding Digitization Efforts Across Library Departments (Jay Trask, Jane Monson, and Jessica Hayden); (24) From Silos to Collaboration (Joyce Meldrem); (25) Key Performance Indicator Tracking Using Google Forms (Joshua Lambert); (26) Bridging the Gap: Providing Equal Access of Library Resources and Services to Distance Learners (Nancy Crabtree, Xiaocan (Lucy) Wang, Bob Black); (27) Coming to the Plains: Latino/a Stories in Nebraska (Laurinda Weisse, Michelle Warren, and Jacob Rosdail); (28) Five Keys to #SocialMediaSuccess in Academic Libraries (Hannah E. Christian and Alison Hanner); (29) Easy Information Literacy Assessments for Small Academic Libraries (Julie Pinnell); (30) Traversing the Path: A Library Director's Guide to the Higher Learning Commission's Open Pathway for Accreditation (Sandy Moore); (31) Drawing Magic: Visualizing the Internet to Introduce Information Literacy (Kelly Leahy); (32) Chatspeak for Librarians: Best Practices for Chat Reference (Tanner D. Lewey); (33) The Creative Learning Spiral: A Python Learner in the Library (Greta Valentine); (34) The Poet's Papers: Literary Research in the Small College Archives (Martha A. Tanner); (35) Giving Students an Edge: Enhancing Resumes with a Digital Information Research Certificate (Rachel R. Vukas); and (36) Where Did You Get That eBook? Comparison of Student/Faculty Use of eBooks, Library Space, and Citation Management Programs (Alice B. Ruleman). (Individual papers contain references.) [For the 2017 proceedings, see ED578189.]

“ERIC ED590389: 2018 Brick & Click: An Academic Library Conference (18th, Maryville, Missouri, November 2, 2018) Sixteen Scholarly Papers And Twenty Abstracts Comprise The Content Of The Eighteenth Annual Brick & Click Libraries Conference, Held Annually At Northwest Missouri State University In Maryville, Missouri. The Proceedings, Authored By Academic Librarians And Presented At The Conference, Portray The Contemporary And Future Face Of Librarianship. The 2018 Paper And Abstract Titles Include: (1) Committee On Diversity & Inclusion: Cultivating An Inclusive Library Environment (Orolando Duffus, Andrea Malone, Margaret Dunn, Lisa Cruces, Matthew Moore, Annie Wu, And Frederick Young); (2) Checking Out The LGBT+ (Kayla Reed); (3) Tailoring Library Instruction To Adult Students: Applying The Science And Methods Of Andragogy For Modern Instructional And Reference Services (Eric Deatherage And Jason Smith); (4) Library-Faculty Collaboration For OER Promotion And Implementation (Paula Martin); (5) The Facts Of Fiction: Research For Creative Writers (Addison Lucchi); (6) Location And The Collection Connection (Kayla Reed And Amber Carr); (7) Gay For No Pay: How To Maintain An LGBTQ+ Collection With No Budget (Rachel Wexelbaum); (8) A Step Up: Piloting Integrated Information Literacy Instruction Throughout A Discipline (Nathan Elwood And Robyn Hartman); (9) Not Just A Collection: The Emergence And Evolution Of Our Contemporary Collection (Hong Li And Kayla Reed); (10) Flipster: How One Community College Library Supports Faculty And Student Academic Needs With Flipster Digital Magazines (Stephen Ambra); (11) Three Ring Circus: A Model For Understanding And Teaching Students About Bias (Virginia Cairns); (12) Demystifying DH: How To Get Started With Digital Humanities (Sherri Brown And Forstot Burke); (13) Academic Libraries Embracing Technology With A Purpose (Lavoris Martin); (14) (A)ffective Management: A People First Management Approach (Ryan Weir); (15) Plugged & Unplugged Active Learning Strategies For One Shots (Judy Bastin, Justina Mollach, Leslie Pierson, Ruth Harries, And Teresa Mayginnes); (16) Giving A Booster Shot To Your One Shot: Incorporating Engaging Activities Into Library Instruction (Kelly Leahy, Gwen Wilson, And Angela Beatie); (17) Adventures With Omeka.net: Metadata, Workflows, And Exhibit-based Storytelling At UNO Libraries (Yumi Ohira, Angela Kroeger, And Lori Schwartz); (18) Online Badge Classes For High School Students (Angela Paul); (19) Fake News: The Fun, The Fear, And The Future Of Resource Evaluation (Lindsay Brownfield); (20) Making Outreach The Library's Mission (April K. Miller); (21) Active Learning For Metaliteracies: Digital Modules From The New Literacies Alliance (Rachel R. Vukas, Prasanna Vaduvathiriyan, And Brenda Linares); (22) Calculating Return On Investment In Libraries (Nicholas Wyant); (23) Crossing Borders: Expanding Digitization Efforts Across Library Departments (Jay Trask, Jane Monson, And Jessica Hayden); (24) From Silos To Collaboration (Joyce Meldrem); (25) Key Performance Indicator Tracking Using Google Forms (Joshua Lambert); (26) Bridging The Gap: Providing Equal Access Of Library Resources And Services To Distance Learners (Nancy Crabtree, Xiaocan (Lucy) Wang, Bob Black); (27) Coming To The Plains: Latino/a Stories In Nebraska (Laurinda Weisse, Michelle Warren, And Jacob Rosdail); (28) Five Keys To #SocialMediaSuccess In Academic Libraries (Hannah E. Christian And Alison Hanner); (29) Easy Information Literacy Assessments For Small Academic Libraries (Julie Pinnell); (30) Traversing The Path: A Library Director's Guide To The Higher Learning Commission's Open Pathway For Accreditation (Sandy Moore); (31) Drawing Magic: Visualizing The Internet To Introduce Information Literacy (Kelly Leahy); (32) Chatspeak For Librarians: Best Practices For Chat Reference (Tanner D. Lewey); (33) The Creative Learning Spiral: A Python Learner In The Library (Greta Valentine); (34) The Poet's Papers: Literary Research In The Small College Archives (Martha A. Tanner); (35) Giving Students An Edge: Enhancing Resumes With A Digital Information Research Certificate (Rachel R. Vukas); And (36) Where Did You Get That EBook? Comparison Of Student/Faculty Use Of EBooks, Library Space, And Citation Management Programs (Alice B. Ruleman). (Individual Papers Contain References.) [For The 2017 Proceedings, See ED578189.]” Metadata:

  • Title: ➤  ERIC ED590389: 2018 Brick & Click: An Academic Library Conference (18th, Maryville, Missouri, November 2, 2018) Sixteen Scholarly Papers And Twenty Abstracts Comprise The Content Of The Eighteenth Annual Brick & Click Libraries Conference, Held Annually At Northwest Missouri State University In Maryville, Missouri. The Proceedings, Authored By Academic Librarians And Presented At The Conference, Portray The Contemporary And Future Face Of Librarianship. The 2018 Paper And Abstract Titles Include: (1) Committee On Diversity & Inclusion: Cultivating An Inclusive Library Environment (Orolando Duffus, Andrea Malone, Margaret Dunn, Lisa Cruces, Matthew Moore, Annie Wu, And Frederick Young); (2) Checking Out The LGBT+ (Kayla Reed); (3) Tailoring Library Instruction To Adult Students: Applying The Science And Methods Of Andragogy For Modern Instructional And Reference Services (Eric Deatherage And Jason Smith); (4) Library-Faculty Collaboration For OER Promotion And Implementation (Paula Martin); (5) The Facts Of Fiction: Research For Creative Writers (Addison Lucchi); (6) Location And The Collection Connection (Kayla Reed And Amber Carr); (7) Gay For No Pay: How To Maintain An LGBTQ+ Collection With No Budget (Rachel Wexelbaum); (8) A Step Up: Piloting Integrated Information Literacy Instruction Throughout A Discipline (Nathan Elwood And Robyn Hartman); (9) Not Just A Collection: The Emergence And Evolution Of Our Contemporary Collection (Hong Li And Kayla Reed); (10) Flipster: How One Community College Library Supports Faculty And Student Academic Needs With Flipster Digital Magazines (Stephen Ambra); (11) Three Ring Circus: A Model For Understanding And Teaching Students About Bias (Virginia Cairns); (12) Demystifying DH: How To Get Started With Digital Humanities (Sherri Brown And Forstot Burke); (13) Academic Libraries Embracing Technology With A Purpose (Lavoris Martin); (14) (A)ffective Management: A People First Management Approach (Ryan Weir); (15) Plugged & Unplugged Active Learning Strategies For One Shots (Judy Bastin, Justina Mollach, Leslie Pierson, Ruth Harries, And Teresa Mayginnes); (16) Giving A Booster Shot To Your One Shot: Incorporating Engaging Activities Into Library Instruction (Kelly Leahy, Gwen Wilson, And Angela Beatie); (17) Adventures With Omeka.net: Metadata, Workflows, And Exhibit-based Storytelling At UNO Libraries (Yumi Ohira, Angela Kroeger, And Lori Schwartz); (18) Online Badge Classes For High School Students (Angela Paul); (19) Fake News: The Fun, The Fear, And The Future Of Resource Evaluation (Lindsay Brownfield); (20) Making Outreach The Library's Mission (April K. Miller); (21) Active Learning For Metaliteracies: Digital Modules From The New Literacies Alliance (Rachel R. Vukas, Prasanna Vaduvathiriyan, And Brenda Linares); (22) Calculating Return On Investment In Libraries (Nicholas Wyant); (23) Crossing Borders: Expanding Digitization Efforts Across Library Departments (Jay Trask, Jane Monson, And Jessica Hayden); (24) From Silos To Collaboration (Joyce Meldrem); (25) Key Performance Indicator Tracking Using Google Forms (Joshua Lambert); (26) Bridging The Gap: Providing Equal Access Of Library Resources And Services To Distance Learners (Nancy Crabtree, Xiaocan (Lucy) Wang, Bob Black); (27) Coming To The Plains: Latino/a Stories In Nebraska (Laurinda Weisse, Michelle Warren, And Jacob Rosdail); (28) Five Keys To #SocialMediaSuccess In Academic Libraries (Hannah E. Christian And Alison Hanner); (29) Easy Information Literacy Assessments For Small Academic Libraries (Julie Pinnell); (30) Traversing The Path: A Library Director's Guide To The Higher Learning Commission's Open Pathway For Accreditation (Sandy Moore); (31) Drawing Magic: Visualizing The Internet To Introduce Information Literacy (Kelly Leahy); (32) Chatspeak For Librarians: Best Practices For Chat Reference (Tanner D. Lewey); (33) The Creative Learning Spiral: A Python Learner In The Library (Greta Valentine); (34) The Poet's Papers: Literary Research In The Small College Archives (Martha A. Tanner); (35) Giving Students An Edge: Enhancing Resumes With A Digital Information Research Certificate (Rachel R. Vukas); And (36) Where Did You Get That EBook? Comparison Of Student/Faculty Use Of EBooks, Library Space, And Citation Management Programs (Alice B. Ruleman). (Individual Papers Contain References.) [For The 2017 Proceedings, See ED578189.]
  • Author:
  • Language: English

“ERIC ED590389: 2018 Brick & Click: An Academic Library Conference (18th, Maryville, Missouri, November 2, 2018) Sixteen Scholarly Papers And Twenty Abstracts Comprise The Content Of The Eighteenth Annual Brick & Click Libraries Conference, Held Annually At Northwest Missouri State University In Maryville, Missouri. The Proceedings, Authored By Academic Librarians And Presented At The Conference, Portray The Contemporary And Future Face Of Librarianship. The 2018 Paper And Abstract Titles Include: (1) Committee On Diversity & Inclusion: Cultivating An Inclusive Library Environment (Orolando Duffus, Andrea Malone, Margaret Dunn, Lisa Cruces, Matthew Moore, Annie Wu, And Frederick Young); (2) Checking Out The LGBT+ (Kayla Reed); (3) Tailoring Library Instruction To Adult Students: Applying The Science And Methods Of Andragogy For Modern Instructional And Reference Services (Eric Deatherage And Jason Smith); (4) Library-Faculty Collaboration For OER Promotion And Implementation (Paula Martin); (5) The Facts Of Fiction: Research For Creative Writers (Addison Lucchi); (6) Location And The Collection Connection (Kayla Reed And Amber Carr); (7) Gay For No Pay: How To Maintain An LGBTQ+ Collection With No Budget (Rachel Wexelbaum); (8) A Step Up: Piloting Integrated Information Literacy Instruction Throughout A Discipline (Nathan Elwood And Robyn Hartman); (9) Not Just A Collection: The Emergence And Evolution Of Our Contemporary Collection (Hong Li And Kayla Reed); (10) Flipster: How One Community College Library Supports Faculty And Student Academic Needs With Flipster Digital Magazines (Stephen Ambra); (11) Three Ring Circus: A Model For Understanding And Teaching Students About Bias (Virginia Cairns); (12) Demystifying DH: How To Get Started With Digital Humanities (Sherri Brown And Forstot Burke); (13) Academic Libraries Embracing Technology With A Purpose (Lavoris Martin); (14) (A)ffective Management: A People First Management Approach (Ryan Weir); (15) Plugged & Unplugged Active Learning Strategies For One Shots (Judy Bastin, Justina Mollach, Leslie Pierson, Ruth Harries, And Teresa Mayginnes); (16) Giving A Booster Shot To Your One Shot: Incorporating Engaging Activities Into Library Instruction (Kelly Leahy, Gwen Wilson, And Angela Beatie); (17) Adventures With Omeka.net: Metadata, Workflows, And Exhibit-based Storytelling At UNO Libraries (Yumi Ohira, Angela Kroeger, And Lori Schwartz); (18) Online Badge Classes For High School Students (Angela Paul); (19) Fake News: The Fun, The Fear, And The Future Of Resource Evaluation (Lindsay Brownfield); (20) Making Outreach The Library's Mission (April K. Miller); (21) Active Learning For Metaliteracies: Digital Modules From The New Literacies Alliance (Rachel R. Vukas, Prasanna Vaduvathiriyan, And Brenda Linares); (22) Calculating Return On Investment In Libraries (Nicholas Wyant); (23) Crossing Borders: Expanding Digitization Efforts Across Library Departments (Jay Trask, Jane Monson, And Jessica Hayden); (24) From Silos To Collaboration (Joyce Meldrem); (25) Key Performance Indicator Tracking Using Google Forms (Joshua Lambert); (26) Bridging The Gap: Providing Equal Access Of Library Resources And Services To Distance Learners (Nancy Crabtree, Xiaocan (Lucy) Wang, Bob Black); (27) Coming To The Plains: Latino/a Stories In Nebraska (Laurinda Weisse, Michelle Warren, And Jacob Rosdail); (28) Five Keys To #SocialMediaSuccess In Academic Libraries (Hannah E. Christian And Alison Hanner); (29) Easy Information Literacy Assessments For Small Academic Libraries (Julie Pinnell); (30) Traversing The Path: A Library Director's Guide To The Higher Learning Commission's Open Pathway For Accreditation (Sandy Moore); (31) Drawing Magic: Visualizing The Internet To Introduce Information Literacy (Kelly Leahy); (32) Chatspeak For Librarians: Best Practices For Chat Reference (Tanner D. Lewey); (33) The Creative Learning Spiral: A Python Learner In The Library (Greta Valentine); (34) The Poet's Papers: Literary Research In The Small College Archives (Martha A. Tanner); (35) Giving Students An Edge: Enhancing Resumes With A Digital Information Research Certificate (Rachel R. Vukas); And (36) Where Did You Get That EBook? Comparison Of Student/Faculty Use Of EBooks, Library Space, And Citation Management Programs (Alice B. Ruleman). (Individual Papers Contain References.) [For The 2017 Proceedings, See ED578189.]” Subjects and Themes:

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The book is available for download in "texts" format, the size of the file-s is: 109.10 Mbs, the file-s for this book were downloaded 114 times, the file-s went public at Wed May 24 2023.

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Find ERIC ED590389: 2018 Brick & Click: An Academic Library Conference (18th, Maryville, Missouri, November 2, 2018) Sixteen Scholarly Papers And Twenty Abstracts Comprise The Content Of The Eighteenth Annual Brick & Click Libraries Conference, Held Annually At Northwest Missouri State University In Maryville, Missouri. The Proceedings, Authored By Academic Librarians And Presented At The Conference, Portray The Contemporary And Future Face Of Librarianship. The 2018 Paper And Abstract Titles Include: (1) Committee On Diversity & Inclusion: Cultivating An Inclusive Library Environment (Orolando Duffus, Andrea Malone, Margaret Dunn, Lisa Cruces, Matthew Moore, Annie Wu, And Frederick Young); (2) Checking Out The LGBT+ (Kayla Reed); (3) Tailoring Library Instruction To Adult Students: Applying The Science And Methods Of Andragogy For Modern Instructional And Reference Services (Eric Deatherage And Jason Smith); (4) Library-Faculty Collaboration For OER Promotion And Implementation (Paula Martin); (5) The Facts Of Fiction: Research For Creative Writers (Addison Lucchi); (6) Location And The Collection Connection (Kayla Reed And Amber Carr); (7) Gay For No Pay: How To Maintain An LGBTQ+ Collection With No Budget (Rachel Wexelbaum); (8) A Step Up: Piloting Integrated Information Literacy Instruction Throughout A Discipline (Nathan Elwood And Robyn Hartman); (9) Not Just A Collection: The Emergence And Evolution Of Our Contemporary Collection (Hong Li And Kayla Reed); (10) Flipster: How One Community College Library Supports Faculty And Student Academic Needs With Flipster Digital Magazines (Stephen Ambra); (11) Three Ring Circus: A Model For Understanding And Teaching Students About Bias (Virginia Cairns); (12) Demystifying DH: How To Get Started With Digital Humanities (Sherri Brown And Forstot Burke); (13) Academic Libraries Embracing Technology With A Purpose (Lavoris Martin); (14) (A)ffective Management: A People First Management Approach (Ryan Weir); (15) Plugged & Unplugged Active Learning Strategies For One Shots (Judy Bastin, Justina Mollach, Leslie Pierson, Ruth Harries, And Teresa Mayginnes); (16) Giving A Booster Shot To Your One Shot: Incorporating Engaging Activities Into Library Instruction (Kelly Leahy, Gwen Wilson, And Angela Beatie); (17) Adventures With Omeka.net: Metadata, Workflows, And Exhibit-based Storytelling At UNO Libraries (Yumi Ohira, Angela Kroeger, And Lori Schwartz); (18) Online Badge Classes For High School Students (Angela Paul); (19) Fake News: The Fun, The Fear, And The Future Of Resource Evaluation (Lindsay Brownfield); (20) Making Outreach The Library's Mission (April K. Miller); (21) Active Learning For Metaliteracies: Digital Modules From The New Literacies Alliance (Rachel R. Vukas, Prasanna Vaduvathiriyan, And Brenda Linares); (22) Calculating Return On Investment In Libraries (Nicholas Wyant); (23) Crossing Borders: Expanding Digitization Efforts Across Library Departments (Jay Trask, Jane Monson, And Jessica Hayden); (24) From Silos To Collaboration (Joyce Meldrem); (25) Key Performance Indicator Tracking Using Google Forms (Joshua Lambert); (26) Bridging The Gap: Providing Equal Access Of Library Resources And Services To Distance Learners (Nancy Crabtree, Xiaocan (Lucy) Wang, Bob Black); (27) Coming To The Plains: Latino/a Stories In Nebraska (Laurinda Weisse, Michelle Warren, And Jacob Rosdail); (28) Five Keys To #SocialMediaSuccess In Academic Libraries (Hannah E. Christian And Alison Hanner); (29) Easy Information Literacy Assessments For Small Academic Libraries (Julie Pinnell); (30) Traversing The Path: A Library Director's Guide To The Higher Learning Commission's Open Pathway For Accreditation (Sandy Moore); (31) Drawing Magic: Visualizing The Internet To Introduce Information Literacy (Kelly Leahy); (32) Chatspeak For Librarians: Best Practices For Chat Reference (Tanner D. Lewey); (33) The Creative Learning Spiral: A Python Learner In The Library (Greta Valentine); (34) The Poet's Papers: Literary Research In The Small College Archives (Martha A. Tanner); (35) Giving Students An Edge: Enhancing Resumes With A Digital Information Research Certificate (Rachel R. Vukas); And (36) Where Did You Get That EBook? Comparison Of Student/Faculty Use Of EBooks, Library Space, And Citation Management Programs (Alice B. Ruleman). (Individual Papers Contain References.) [For The 2017 Proceedings, See ED578189.] at online marketplaces:


45[EuroPython 2019] Thomas Kluiters - Securely Executing Python Machine Learning Models With Distroless Images At ING

Executing machine learning models in a production environment can be tricky, especially at a major bank where compliance and risk are carefully taken into account. In this talk I explain how, we, at ING (a large bank operating on global scale), execute our Python models in a production environment by building minimal Docker images for python versions. I will first talk about the possible security risks of running any docker container in a production environment. Then I will talk about ways in which we can make Docker containers more secure by building minimal docker images for Python. Finally I will explain how these docker images are used in practice to serve machine learning models at ING. Prerequisites: - Some basic knowledge of Docker can be helpful - Some basic understanding of security can be helpful Goals: - Understand the security risks of running docker containers - Know how to make docker images more secure - How to build secure model serving docker images Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

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46Learning Predictive Analytics With Python : Gain Practical Insights Into Predictive Modelling By Implementing Predictive Analytics Algorithms On Public Datasets With Python

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Executing machine learning models in a production environment can be tricky, especially at a major bank where compliance and risk are carefully taken into account. In this talk I explain how, we, at ING (a large bank operating on global scale), execute our Python models in a production environment by building minimal Docker images for python versions. I will first talk about the possible security risks of running any docker container in a production environment. Then I will talk about ways in which we can make Docker containers more secure by building minimal docker images for Python. Finally I will explain how these docker images are used in practice to serve machine learning models at ING. Prerequisites: - Some basic knowledge of Docker can be helpful - Some basic understanding of security can be helpful Goals: - Understand the security risks of running docker containers - Know how to make docker images more secure - How to build secure model serving docker images Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

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47WARC: Www.johnwittenauer.net-machine-learning-exercises-in-python-part-1 20160813

Executing machine learning models in a production environment can be tricky, especially at a major bank where compliance and risk are carefully taken into account. In this talk I explain how, we, at ING (a large bank operating on global scale), execute our Python models in a production environment by building minimal Docker images for python versions. I will first talk about the possible security risks of running any docker container in a production environment. Then I will talk about ways in which we can make Docker containers more secure by building minimal docker images for Python. Finally I will explain how these docker images are used in practice to serve machine learning models at ING. Prerequisites: - Some basic knowledge of Docker can be helpful - Some basic understanding of security can be helpful Goals: - Understand the security risks of running docker containers - Know how to make docker images more secure - How to build secure model serving docker images Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

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48Hak5 - Learning Python, VBS Scripts And Trivia! Hak5 1022.3

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This time on the show, pound include programming dot h! We're going to void main and Hello World all up in this biznitch. Python, JavaScript, BASIC? It's time to learn to code! All that and more this time on Hak5............return zero Source: https://www.youtube.com/watch?v=AmNZwW6833U Uploader: Hak5

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49Crypto Currency Price Prediction With Machine Learning Using Python

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We use and study a wide range of machine learning methods to predict and trade in the daily crypto currency market. We teach the algorithms to make daily market predictions based on how the 100 cryptocurrencies with the most market value change in price. Based on our research, all of the used models are able to make estimates that are statistically sound, with the average accuracy of all crypto currencies falling between 52.9% and 54.1%. When these accurate numbers are based on the 10% most confident expectations for each class and day, they go up to somewhere between 57.5% and 59.5%. A well-known case study in the field of data science looks at how people try to figure out how much different digital currencies are worth. Stock prices and the prices of cryptocurrencies are based on more than just the amount of buy and sell orders. At the moment, the government's financial policies about digital currencies affect how the prices of these things change. People's views about a crypto currency or a star who directly or indirectly backs a crypto currency can also cause a big rise in buying and selling of that currency. This study looks at the trustworthiness of the three most famous coins on the market today: bitcoin, how well buying strategies for ethereum and litecoin that are based on machine learning work. The models are checked and tested with both good and bad market situations. This lets us figure out how accurate the forecasts are in light of any changes in how the market feels between the proof and test times.

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50PYTHON : L'ESSENTIEL POUR MACHINE LEARNING - ML#7

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Introduction à la programmation Python pour faire du Machine Learning. Python est un langage interprété, et c'est le meilleur pour faire du Machine Learning en 2019 : il contient le plus de fonctionnalité et de ressource pour exceller en Machine Learning et en Deep Learning. Google, Facebook, Amazon utilisent tous Python pour leurs initiatives d'intelligence artificielle. Dans cette vidéo, je vous montre les structures de contrôle de base qu'il faut maîtriser pour écrire des algorithmes de machine learning. ► Timecode de la vidéo : 00:00 : Début 0:26 : Introduction 1:58 : Les Bases de Python 2:57 : Commentaires, Variables, Print 5:04 : IF - ELIF - ELSE 7:42 : Astuce Bonus 8:10 : Jupyter Notebook 9:27 : FOR LOOP 10:40 : WHILE LOOP 12:10 : Importer des Modules 14:02 : Créer nos propres fonctions ► Lien pour télécharger Anaconda : https://www.anaconda.com ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: https://machinelearnia.com/ ► Soutenez-moi sur Tipeee pour du contenu BONUS: https://fr.tipeee.com/machine-learnia ► REJOINS NOTRE COMMUNAUTÉ DISCORD https://discord.gg/WMvHpzu ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: https://machinelearnia.com/apprendre-le-machine-learning-en-une-semaine/ ► Pour En Savoir plus : Visitez Machine Learnia : https://machinelearnia.com/ ► Qui suis-je ? Je m’appelle Guillaume Saint-Cirgue et je suis Data Scientist au Royaume Uni. Après avoir suivi un parcours classique maths sup maths spé et avoir intégré une bonne école d’ingénieur, je me suis tourné vers l’intelligence artificielle de ma propre initiative et j’ai commencé à apprendre tout seul le machine learning et le deep learning en suivant des formations payantes, en lisant des articles scientifiques, en suivant les cours du MIT et de Stanford et en passant des week end entier à développer mes propres codes. Aujourd’hui, je veux vous offrir ce que j’ai appris gratuitement car le monde a urgemment besoin de se former en Intelligence Artificielle. Que vous souhaitiez changer de vie, de carrière, ou bien développer vos compétences à résoudre des problèmes, ma chaîne vous y aidera. C’est votre tour de passer à l’action ! ► Une question ? Contactez-moi: [email protected]

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1Learning Python

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  • Title: Learning Python
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  • Languages: English - ger
  • Number of Pages: Median: 591
  • Publisher: ➤  O'Reilly Media - O'Reilly Media, Incorporated - O'Reilly - Shroff Publishers & Distributors Pvt. Ltd. - CreateSpace Independent Publishing Platform - O'Reilly Media, Inc.
  • Publish Date: ➤  
  • Publish Location: ➤  Sebstopol - Sebastopol, CA - Cambridge - Taipei - Tokyo - Farnham - Paris - Köln - Beijing - CA 95472

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"Dieses Buch bietet Ihnen eine kurze Einführung in die Programmiersprache Python."

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  • First Year Published: 1999
  • Is Full Text Available: Yes
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  • Access Status: Borrowable

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2Learning Python Manual (4th edition)(Chinese Edition)

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  • First Year Published: 2011
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1Susan B. Anthony Rebel, Crusader, Humanitarian

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Alma Lutz's outstanding biography of Susan B. Anthony is revered for its descriptive power, attention to detail and historical significance to the women's Suffragette movement. - Summary by PhyllisV

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  • Number of Sections: 26
  • Total Time: 14:16:52

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