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Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (43 - Model Selection)

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)

The official online book is at  http://openbookproject.net/thinkcs/python/english3e/

The official online book is at  http://openbookproject.net/thinkcs/python/english3e/

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

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

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 (18 - Support Vector Machine (SVM))

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

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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'

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

Learn Python from start to end

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

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (31 - Part 6 Reinforcement Learning)

This week I review resources aimed at teaching you Python

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_-_2019-03-23_17-47-51/rasbt-python-machine-learning-book-2nd-edition_-_2019-03-23_17-47-51.bundle and run: git clone rasbt-python-machine-learning-book-2nd-edition_-_2019-03-23_17-47-51.bundle Source: https://github.com/rasbt/python-machine-learning-book-2nd-edition Uploader: rasbt Upload date: 2019-03-23

Course in Machine Learning and Data Science

People talk about a Moore's Law for gene sequencing, a Moore's Law for software, etc. This is talk is about *the* Moore's Law, the bull that the other "Laws" ride; and how Python-powered ML helps drive it. How do we keep making ever-smaller devices? How do we harness atomic-scale physics? Large-scale machine learning is key. The computation drives new chip designs, and those new chip designs are used for new computations, ad infinitum. High-dimensional regression, classification, active learning, optimization, ranking, clustering, density estimation, scientific visualization, massively parallel processing -- it all comes into play, and Python is powering it all.

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'

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, D. Stansby): [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

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

https://2019.za.pycon.org/talks/31-machine-learning-engineering-principles-with-python-and-mlflow/ Machine Learning is a very hyped topic of the moment. While a lot of the talks and presentations cover the data science component, very few cover the nity gritty details of a machine learning pipeline. This talk will focus on the engineering part of Machine Learning by covering different Machine Learning systems architecture best practices, strategies including design. We will delve into the essence of Uber's Michelangelo, Airbnbs s Bighead and Facebooks FB Learner. During the talk, I will use MLFlow and Python as platforms to create an open-source based solution similar to the ones from the big tech companies for the everyday tech startup. The entirety of the cycle of training, deployment, monitoring, champion/challenger testing, and serving layer will be addressed. Technical debt prevention is another topic that will be addressed in the end of the talk. Room: Ballroom

48 pages ; 28 cm

Summary: I discuss how I use Python and some of the cool modules and libraries that I've found Source: http://hackerpublicradio.org/eps.php?id=1824 I'm Learning Some Python Lately I'm finally getting around to learning some Python. I wouldn't go as far as to say I'm learning it properly—that's not really my way—I'm kind of poking around in the dark learning things on an "as-needed" basis, but I'm finding that it's incredibly powerful and making me much more efficient in my daily life. In this podcast I discuss some of my favorite ways of using it and some of the cool modules and libraries that I've found that make things surprisingly easy in Python that used to be difficult for me in bash . What I Use It For Website build scripts, both for the School of Music and for my personal website. Converted from bash, tested and working fine on Windows and Mac. Text manipulation scripts, used in conjuction with blather. These do things like change text case, remove spaces, and so forth. Text entry. Voice commands insert various kinds of text templates or canned email responses for my classes. Also used in conjunction with blather. Adding or stripping HTML tags to/from selected text. Getting current weather conditions and forecasts, having results spoken back to me using system text-to-speech engine. Fun blather commands where I interact with my computer and have it talk back to me. Favorite Python Modules/Libraries pyperclip A cross-platform clipboard module for Python. (only handles plain text for now) https://pypi.python.org/pypi/pyperclip/1.5.11 pyttsx A Python package supporting common text-to-speech engines on Mac OS X, Windows, and Linux. https://pypi.python.org/pypi/pyttsx bs4 HTML parsing library. Beautiful Soup Documentation htmlmin A configurable HTML Minifier with safety features. https://pypi.python.org/pypi/htmlmin/ smartypants smartypants is a Python fork of SmartyPants , which easily translates "plain" ASCII punctuation characters into “smart” typographic punctuation HTML entities. titlecase Changes all words to Title Caps, and attempts to be clever about SMALL words like a/an/the in the input. https://pypi.python.org/pypi/titlecase swnamer A name generator that uses Star Wars characters, species and planets to create un fisique names. https://pypi.python.org/pypi/swnamer/0.1.0 Demo Screencasts Blather + Python: Insert Text from Predefined Nested Lists Using Voice Commands: https://www.youtube.com/watch?v=6futHS4JLsU Blather: "too much coffee?" python script: https://www.youtube.com/watch?v=GTDMi1zF76c

Machine Learning de A a la Z - R y Python para Data Science (2020)(Udemy)

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.

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.]

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/

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

random3, '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'

In questo video parleremo di un tema molto importante: come strutturare correttamente un progetto Python o Machine Learning.Strutturare correttamente un progetto Python serve prima di tutto a dare un senso a quello che state facendo e a rendere il codice immediatamente comprensibile, soprattutto a distanza di tempo, per poi stessi e anche per chi dovrà lavorare al vostro stesso progetto e quindi al vostro codice.Infatti, se avete del codice abbastanza \"incasinato\" e non strutturato correttamente, nel caso qualcuno (collaboratore, amico, conoscente, ecc.) dovesse aiutarvi nella ricerca di bugs o nell'implementazione di nuove funzionalità, dovrà prima di tutto capire come funziona il vostro programma, quali sono i flussi del software e come esso e organizzato. Risulterà quindi un'enorme perdita di tempo. Invece di concentrarvi nella vera risoluzione di un bug, dovrete prima di tutto cercare di capire come funziona il codice.Un progetto ben strutturato, e quindi anche un codice ben organizzato, vi consentirà un agevole refactor e un'agevole implementazione di nuove funzionalità.

I am testing

Programmers may be hesitant to use declarative systems, because of the associated learning curve. In this paper, we present an API that integrates the IDP Knowledge Base system into the Python programming language. IDP is a state-of-the-art logical system, which uses SAT, SMT, Logic Programming and Answer Set Programming technology. Python is currently one of the most widely used (teaching) languages for programming. The first goal of our API is to allow a Python programmer to use the declarative power of IDP, without needing to learn any new syntax or semantics. The second goal is allow IDP to be added to/removed from an existing code base with minimal changes.

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Python For Data Science And Machine Learning Bootcamp

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

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 (38 - Part 9 Dimensionality Reduction)

Power of Machine Learning with python

Power of Machine Learning with python

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 (45 - Exclusive Offer)

This is a beginner level book.

Nathan Brouwer https://www.pyohio.org/2024/program/talks/23-python-and-me-using-machine-learning-in-python-to-analyze We are over 20 years into the genomics era, with new insights into human health and our recent evolutionary history emerging almost daily. Genomics may seem like the province of PhDs and R\&D departments, but anyone with basic Python skills can navigate a genomics data pipeline and explore human genetic diversity, including their own! In this presentation, I will introduce the fields of population and health genomics, and the types of domain\-specific data used for genomics study. I will then demonstrate how you can use Python to visualize and analyze public data sources like the 1000 Genomes Project using unsupervised machine learning methods, and how to investigate your own genomics data from sources like *23andMe*. Since many people are not comfortable giving for\-profit companies access to their genomic data, I'll also show how you can simulate realistic personal genomic data via a supervised ML model. #PyOhio #Python The 17th annual PyOhio held July 27-28 in Cleveland, OH. === https://PyOhio.org Founded in 2008, PyOhio is a free annual Python programming language community conference based in Ohio. Content ranges from beginner to advanced and is intended to be relevant to all types of Python users: students, software professionals, scientists, hobbyists, and anyone looking to learn more. Sun Jul 28 14:45:00 2024 at Calypso Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Nathan Brouwer https://www.pyohio.org/2024/program/talks/23-python-and-me-using-machine-learning-in-python-to-analyze We are over 20 years into the genomics era, with new insights into human health and our recent evolutionary history emerging almost daily. Genomics may seem like the province of PhDs and R\&D departments, but anyone with basic Python skills can navigate a genomics data pipeline and explore human genetic diversity, including their own! In this presentation, I will introduce the fields of population and health genomics, and the types of domain\-specific data used for genomics study. I will then demonstrate how you can use Python to visualize and analyze public data sources like the 1000 Genomes Project using unsupervised machine learning methods, and how to investigate your own genomics data from sources like *23andMe*. Since many people are not comfortable giving for\-profit companies access to their genomic data, I'll also show how you can simulate realistic personal genomic data via a supervised ML model. #PyOhio #Python The 17th annual PyOhio held July 27-28 in Cleveland, OH. === https://PyOhio.org Founded in 2008, PyOhio is a free annual Python programming language community conference based in Ohio. Content ranges from beginner to advanced and is intended to be relevant to all types of Python users: students, software professionals, scientists, hobbyists, and anyone looking to learn more. Sun Jul 28 14:45:00 2024 at Calypso Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Nathan Brouwer https://www.pyohio.org/2024/program/talks/23-python-and-me-using-machine-learning-in-python-to-analyze We are over 20 years into the genomics era, with new insights into human health and our recent evolutionary history emerging almost daily. Genomics may seem like the province of PhDs and R\&D departments, but anyone with basic Python skills can navigate a genomics data pipeline and explore human genetic diversity, including their own! In this presentation, I will introduce the fields of population and health genomics, and the types of domain\-specific data used for genomics study. I will then demonstrate how you can use Python to visualize and analyze public data sources like the 1000 Genomes Project using unsupervised machine learning methods, and how to investigate your own genomics data from sources like *23andMe*. Since many people are not comfortable giving for\-profit companies access to their genomic data, I'll also show how you can simulate realistic personal genomic data via a supervised ML model. #PyOhio #Python The 17th annual PyOhio held July 27-28 in Cleveland, OH. === https://PyOhio.org Founded in 2008, PyOhio is a free annual Python programming language community conference based in Ohio. Content ranges from beginner to advanced and is intended to be relevant to all types of Python users: students, software professionals, scientists, hobbyists, and anyone looking to learn more. Sun Jul 28 14:45:00 2024 at Calypso Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Nathan Brouwer https://www.pyohio.org/2024/program/talks/23-python-and-me-using-machine-learning-in-python-to-analyze We are over 20 years into the genomics era, with new insights into human health and our recent evolutionary history emerging almost daily. Genomics may seem like the province of PhDs and R\&D departments, but anyone with basic Python skills can navigate a genomics data pipeline and explore human genetic diversity, including their own! In this presentation, I will introduce the fields of population and health genomics, and the types of domain\-specific data used for genomics study. I will then demonstrate how you can use Python to visualize and analyze public data sources like the 1000 Genomes Project using unsupervised machine learning methods, and how to investigate your own genomics data from sources like *23andMe*. Since many people are not comfortable giving for\-profit companies access to their genomic data, I'll also show how you can simulate realistic personal genomic data via a supervised ML model. #PyOhio #Python The 17th annual PyOhio held July 27-28 in Cleveland, OH. === https://PyOhio.org Founded in 2008, PyOhio is a free annual Python programming language community conference based in Ohio. Content ranges from beginner to advanced and is intended to be relevant to all types of Python users: students, software professionals, scientists, hobbyists, and anyone looking to learn more. Sun Jul 28 14:45:00 2024 at Calypso Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1