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Ken Youens-Clark, author of Tiny Python Projects discusses Python. Felienne spoke with Youens-Clark about new features in Python, including optional typing and immutable datatypes. They also discussed Youens-Clark's upcoming book on how to teach Python and testing. Should testing be taught to new programmers from the start, and how to do that? Contexts for programming [...]

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.

Perma.cc archive of https://scikit-learn.org/stable/ created on 2022-07-01 10:56:58.596768+00:00.

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (33 - Thompson Sampling)

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

https://2019.za.pycon.org/talks/80-getting-machine-learning-models-ready-for-production-using-python/ As a Scientist, it’s incredibly satisfying to be given the freedom to experiment by applying new research and rapidly prototyping. This satisfaction can be sustained quite well in a lab environment but can diminish quickly in a corporate environment. This is because of the underlying commercial value motive which science is driven by in a business setting — if it doesn’t add business value to employees or customers, there’s no place for it! Business value, however, goes beyond just being a nifty experiment which shows potential value to employees or customers. In the context of Machine Learning models, the only [business] valuable models, are models in Production! In this talk, I will take the audience through the steps involved in moving from experiments in Jupyter Notebooks to automated model training, serving and deployments for Production using an array of Python tools such as Numpy, Pandas, SciKit Learn and Docker. The intended audience for this talk includes Data Scientists, Software Engineers and any other Data practitioners who have or want to go through the journey of gaining real-time value from Machine Learning models in Production. This talk will impart lessons learnt in moving from Jupyter experiments to writing production-ready Python code, as well as impart important Python tools, frameworks and libraries which can be used to accelerate such a transition. Room: Ballroom

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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"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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Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (35 - Part 8 Deep Learning)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (35 - Part 8 Deep Learning)

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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.

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

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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48 pages ; 28 cm

48 pages ; 28 cm

This week I review resources aimed at teaching you Python

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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'

Course in Machine Learning and Data Science

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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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Perma.cc archive of https://www.analyticsvidhya.com/blog/2018/09/deep-learning-video-classification-python/? created on 2022-09-23 02:38:18.162776+00:00.

130 pages : 23 cm

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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. Know more - https://stackify.com/

Python For Computer Vision With Open CV And Deep Learning

Deep Learning With Python

Python Machine Learning Third Edition Machine Learning and Deep Learning with Python,  scikit-learn, and TensorFlow 2 Sebastian Raschka Vahid Mirjalili

Humans communicate with one another using body language (gestures), such as hand and head gestures, facial expressions, lip movements, and so forth, or through natural language channels like words and writing. Sign language comprehension is just as crucial as knowing normal language. The primary means of communication for those who are hard of hearing is sign language. Without a translation, speaking with other hearing people can be difficult for those with hearing impairments. Because of this, the social lives of deaf people would be greatly improved by the installation of a system that recognizes sign language. In order to recognize the features of the hand in pictures captured by a webcam, we have presented in this study a marker-free, visual American Sign Language recognition system that makes use of image processing, computer vision, and neural network techniques. This paper deals with full phrase gestures that are used regularly every day and methods used to converted them to text. A number of image processing techniques have been used to identify the hand shape from continuous pictures. The Haar Cascade Classifier is used to determine the interpretation of signs and their associated meaning.

This article explains and demonstrates the Mean Shift Algorithm in Python which is an Unsupervised Classification Machine Learning algorithm.

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Cours Machine Learning Python prévu le 3 septembre 2019 ! Formation de 30 vidéos (1 vidéo par jour) pour vous former à Python, la programmation, Numpy Sklearn, Scipy, Pandas, Matplotlib, les algorithmes, visualisation de données et bien plus encore... mais en ce concentrant à fond sur le machine learning et le Data Science ! ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: https://machinelearnia.com/apprendre-le-machine-learning-en-une-semaine/ ► 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 ► Abonnez-vous : https://www.youtube.com/channel/UCmpptkXu8iIFe6kfDK5o7VQ?view_as=subscriber ► 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]

Deep Learning Prerequisites The Numpy Stack In Python ( V 2+)

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PDF BOOK

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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