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This article will provide us with insight and a practical example of Unsupervised Machine Learning using Python. Specifically, we will look at the K-Means Clustering algorithm in the domain of machine learning.

random2, '2-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'

random2, '2-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'

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (08 - Polynomial Regression)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (22 - Random Forest Classification)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (22 - Random Forest Classification)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (22 - Random Forest Classification)

Industrial Machine Learning This talk will provide key insights on the learnings I have obtained throughout my career building & deploying machine learning systems in critical environments across several sectors. I will provide a deep dive on how to build scalable and distributed machine learning data pipelines using Airflow with a Celery backend. I will also compare Airflow with other technologies available out there and how it differentiates, such as Luigi, Chronos, Pinball, etc. If you attend the talk, you will obtain an understanding on the solid fundamentals of Airflow, together with its caveats and walk-arounds for more complex use-cases. As we proceed with the examples, I will cover the challenges that you will run into when scaling Machine Learning systems, and how Airflow can be used to address these using a manager-worker-queue architecture for distributed processing with Celery. By the end of this talk you will have the knowledge required to build your own industry-ready machine learning pipelines to process data at scale, and I will provide further reading resources so people are able to implement the knowledge obtained almost right away. Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/

[ Reading] After Work Data Science Ensemble Learning With Python

learning programming with python in persian

Tobias Brandt https://2016.za.pycon.org/talks/16/ This talk will give an introduction to Neural Networks and Deep Learning in Python. We will cover some of the history of Neural Networks and obstacles that were encountered in the 1990s. This will then lead onto the developments in 2006 and 2012 that lead to the resurgence of interest in Neural Networks and the rebranding of the field as Deep Learning. These developments will be illustrated by means of an extended example of building a classifier of hand written digits on the MNIST dataset. We will start with a simple Multi-Layer Perceptron and then build this up into a Stacked Denoising Autoencoder. All code will be developed using the Keras framework and TensorFlow and can be run on a simple laptop.

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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/

Practical Machine Learning By Example In Python

Python Machine Learning The Ultimate Beginners Guide to Learn Python Machine Learning Step by Step using Scikit-Learn by Reed, Mark

Learn how to Code:

Shrey Somaiya https://2020.pycon.org.au/program/73HFNJ This talk is simply one about my experience of learning C from a python background - what python features helped me understand C better (control flow, algorithm and problem breakdown design, functions, etc) - what C features helped me understand python better (ie pointers in C helped me understand python lists, etc) - how I use my python knowledge to teach C at University python, pycon, australia, programming, conference, technical, pyconline, developers, panel, sessions, libraries, frameworks, community, sysadmins, students, education, data, science PyCon AU is the national conference for the Python programming community, bringing together professional, student and enthusiast developers, sysadmins and operations folk, students, educators, scientists, statisticians, and many others besides, all with a love for working with Python. PyCon AU informs the country's Python developers with presentations, tutorials and panel sessions by experts and core developers of Python, as well as the libraries and frameworks that they rely on. PyCon AU is the national conference for the Python programming community, bringing together professional, student and enthusiast developers, sysadmins and operations folk, students, educators, scientists, statisticians, and many others besides, all with a love for working with Python. PyCon AU informs the country's Python developers with presentations, tutorials and panel sessions by experts and core developers of Python, as well as the libraries and frameworks that they rely on. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Python, PyCon, PyConAU, PyConline Fri Sep 4 10:25:00 2020 at Floperator

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (15 - Part 3 Classification)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (32 - Upper Confidence Bound (UCB))

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (34 - Part 7 Natural Language Processing)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (38 - Part 9 Dimensionality Reduction)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (12 - Evaluating Regression Models Performance)

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]

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]

This article is from Frontiers in Neuroinformatics , volume 8 . Abstract The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

Deep Learning Prerequisites Linear Regression in Python

Programming has become a fundamental skill in the 21st century. Its uses are not only limited to computer science but a wide array of fields. We believe that the 7th-grade kids are mentally capable to learn python online , begin their programming journey, develop interest, be comfortable in handling large chunks of data and also solve complex problems.

Programming has become a fundamental skill in the 21st century. Its uses are not only limited to computer science but a wide array of fields. We believe that the 7th-grade kids are mentally capable to learn python online , begin their programming journey, develop interest, be comfortable in handling large chunks of data and also solve complex problems.

Power of Machine Learning with python

This is a beginner level book.

This is a beginner level book.

My library

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

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

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

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

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

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (40 - Linear Discriminant Analysis (LDA))

Tensor methods are gaining increasing traction in machine learning. However, there are scant to no resources available to perform tensor learning and decomposition in Python. To answer this need we developed TensorLy. TensorLy is a state of the art general purpose library for tensor learning. Written in Python, it aims at following the same standards adopted by the main projects of the Python scientific community and fully integrating with these. It allows for fast and straightforward tensor decomposition and learning and comes with exhaustive tests, thorough documentation and minimal dependencies. It can be easily extended and its BSD licence makes it suitable for both academic and commercial applications. TensorLy is available at https://github.com/tensorly/tensorly

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

"Mastering Git Switch, Python Learning Sites, and Debugging Ruby on Rails" is a comprehensive guide for developers aiming to enhance their skills in essential areas of modern software development. This blog covers three critical topics: mastering the git switch command in Git for efficient branch management, exploring the best Python learning sites for programmers, and delving into advanced debugging techniques for Ruby on Rails applications. Whether you're navigating version control, expanding your programming knowledge, or tackling complex Rails bugs, this guide offers valuable insights and practical tips to boost your productivity and expertise. Read more -  https://stackify.com/

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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

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (37 - Convolutional Neural Networks)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (07 - Multiple Linear Regression)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (20 - Naive Bayes)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (28 -  Part 5 Association Rule Learning)