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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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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'

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

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

Udemy Machine Learning A to Z Python and R in Data Science 2023 (32-33)

Machine Learning With Python- Logistic Regression. This article demonstrates how to utilize the logistic regression classification algorithm in Python.

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'

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.

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

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'

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

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

Course in Machine Learning and Data Science

Dive deep into the world of AI with our Deep Learning with Python and PyTorch. Enroll at BITA Academy for a promising AI career

Python for Computer Vision By Jose Poritla

48 pages ; 28 cm

Cette Formation Python Numpy est un tutoriel français spécial machine learning: Numpy est le package python le plus important pour faire du machine learning et du data science. Numpy comprend le tableau array dit ndarray (n dimensions) qui est un objet extrêmement puissant en machine learning et data science. Numpy propose beaucoup de méthode pour le ndarray, dans cette vidéo nous voyons les différents constructeurs qui permettent d'initialiser les tableau ndarray: np.array() np.zeros() np.ones() np.full() np.random.randn() les deux attributs les plus importants à retenir sont : shape size pour développer des programmes puissants, pensez à définir le type de valeur dans le np.array() avec dtype = np.int16, np.float64 Nous voyons aussi les méthodes les plus utiles pour manipuler la forme de nos tableau numpy: np.vstack np.hstack np.concatenate np.reshape np.squeeze np.ravel Il n'y a rien de plus à retenir pour bien se lancer avec Numpy. Ignorez les autres attributs et méthodes pour le moment ! ► Timeline de la vidéo : 0:00 Intro 00:40 Le tableau Numpy, ses dimensions et sa shape 05:20 initialiser un ndarray: np.ones, np.zeros, 09:15 np.random.randn 12:04 np.linspace, np.arange 13:24 dtype=np.float16 np.float64 15:43 Assembler des tableaux: vstack hstack concatenate 18:40 np.reshape np.squeeze 22:10 np.ravel() 23:08 Exercice ► Soutenez-moi sur Tipeee pour du contenu BONUS: https://fr.tipeee.com/machine-learnia ► Documentation Numpy pour ndarray: https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html ► Documentation Numpy pour np.random: https://docs.scipy.org/doc/numpy-1.16.0/reference/routines.random.html ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: https://machinelearnia.com/ ► 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/ ► Télécharger gratuitement mes codes sur github: https://github.com/MachineLearnia ► Abonnez-vous : https://www.youtube.com/channel/UCmpptkXu8iIFe6kfDK5o7VQ ► 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]

The "Python Machine Learning (3nd edition)" book code repository Python Machine Learning (3rd Ed.) Code Repository Code repositories for the 1st and 2nd edition are available at https://github.com/rasbt/python-machine-learning-book and https://github.com/rasbt/python-machine-learning-book-2nd-edition Python Machine Learning, 3rd Ed. to be published December 9th, 2019 Paperback: 748 pages Publisher: Packt Publishing Language: English ISBN-10: 1789955750 ISBN-13: 978-1789955750 Kindle ASIN: B07VBLX2W7 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 Please note that these are just the code examples accompanying the book, which we 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 ] Training Machine Learning Algorithms for Classification [ open dir ] A Tour of Machine Learning Classifiers Using Scikit-Learn [ open dir ] Building Good Training Sets – Data Pre-Processing [ open dir ] Compressing Data via Dimensionality Reduction [ open dir ] Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[open dir](ch06)] Combining Different Models for Ensemble Learning [ open dir ] Applying Machine Learning to Sentiment Analysis [ open dir ] Embedding a Machine Learning Model into a Web Application [ open dir ] Predicting Continuous Target Variables with Regression Analysis [ open dir ] Working with Unlabeled Data – Clustering Analysis [ open dir ] Implementing a Multi-layer Artificial Neural Network from Scratch [ open dir ] Parallelizing Neural Network Training with TensorFlow [ open dir ] Going Deeper: The Mechanics of TensorFlow [ open dir ] Classifying Images with Deep Convolutional Neural Networks [ open dir ] Modeling Sequential Data Using Recurrent Neural Networks [ open dir ] Generative Adversarial Networks for Synthesizing New Data [ open dir ] Reinforcement Learning for Decision Making in Complex Environments [ open dir ] Raschka, Sebastian, and Vahid Mirjalili. Python Machine Learning, 3rd Ed . Packt Publishing, 2019. @book{RaschkaMirjalili2019, address = {Birmingham, UK}, author = {Raschka, Sebastian and Mirjalili, Vahid}, edition = {3}, isbn = {978-1789955750}, publisher = {Packt Publishing}, title = {{Python Machine Learning, 3rd Ed.}}, year = {2019} } To restore the repository download the bundle wget https://archive.org/download/github.com-rasbt-python-machine-learning-book-3rd-edition_-_2019-12-06_17-19-39/rasbt-python-machine-learning-book-3rd-edition_-_2019-12-06_17-19-39.bundle and run: git clone rasbt-python-machine-learning-book-3rd-edition_-_2019-12-06_17-19-39.bundle Source: https://github.com/rasbt/python-machine-learning-book-3rd-edition Uploader: rasbt Upload date: 2019-12-06

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.

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

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

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.

Python For Data Science And Machine Learning Bootcamp

Ce tutoriel python francais vous présente SKLEARN, le meilleur package pour faire du machine learning avec Python. Tous les modèles, et tous les algorithmes de machine learning ont déjà été implémentés avec une architecture orientée objet, chaque modèle disposant de sa propre classe. KNN, LinearRegression, Decision Trees, Support vector machines, etc. Pour créer un modèle, on génère un objet de la classe correspondante. Au passage, c’est ce qu’on appelle un estimateur (dans sklearn) On peut aussi préciser entre parenthèse les hyper-paramètres de notre modèle. Par exemple, le learning rate d’une descente de gradient, ou bien le nombre d’arbres dans une Random Forest. Une fois qu’on a initialisé notre modèle, on va pouvoir entraîner, l’évaluer, et l’utiliser grâce a trois méthodes qu’on retrouve dans toutes les classes de Sklearn. Ce sont les méthodes Fit, score et predict. ► EXEMPLE Régression Linéaire from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y) model.score(X, y) model.predict(X) ► EXEMPLE K-Nearest Neighbors from sklearn.neighbors import KNeighborClassifier model = KNeighborsClassifier() model.fit(X, y) model.score(X, y) model.predict(X) ► TIMECODE DE LA VIDEO: 0:00 : Intro 01:00 : Comprendre le Machine Learning et L'apprentissage supervisé 04:25 : SKLEARN, API, et le fonctionnement d'un estimateur 08:27 : Régression avec SKLEARN (LinearRegression + SVR) 11:47 : Classification avec SKLEARN (K-Nearest Neighbor) 15:30 : Auriez-vous survécu au TItanic ? + Exercice SKLEARN https://scikit-learn.org/stable/ Carte des algorithmes de SKLEARN https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html ► Me soutenir financièrement sur Tipeee ou Utip (et obtenir des vidéos BONUS) https://fr.tipeee.com/machine-learnia https://utip.io/machinelearnia/ ► MON SITE INTERNET EN COMPLÉMENT DE CETTE VIDÉO: https://machinelearnia.com/ ► 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/ ► Télécharger gratuitement mes codes sur github: https://github.com/MachineLearnia ► Abonnez-vous : https://www.youtube.com/channel/UCmpptkXu8iIFe6kfDK5o7VQ ► 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]

Formation Python spéciale Machine Learning francais. Apprendre Python en 30 vidéos qui contiennent une formation sur Numpy, Pandas, Matplotlib, Scipy, Sklearn, Seaborn, H5py, et bien d'autres techniques. Python est le langage d'excellence pour le machine learning, le deep learning, et la data science. En regardant cette série, vous deviendrez un expert python pour ces domaines en particulier. Merci de vous abonner ! 0:00 : Bienvenue dans cette formation gratuite ! 1:05 : Qui-suis je ? 2:00 : Programme de cette formation de 30 vidéos 3:44 : Comment Installer Python sur votre Ordinateur (Anaconda) 5:11 : Tutoriel Spyder 6:00 : Tutoriel Jupyter Notebook 6:25 : Installer des packages dans Anaconda 7:39 : Invitez-vos amis a vous rejoindre dans cette formation :) ► Le site d'Anaconda a été mis a jour depuis ma video, voici le lien pour télécharger Anaconda : https://www.anaconda.com/products/individual ► Me soutenir financierement sur Tipeee ou Utip (et obtenir des vidéos BONUS) https://fr.tipeee.com/machine-learnia https://utip.io/machinelearnia/ ► REJOINS NOTRE COMMUNAUTÉ DISCORD https://discord.gg/WMvHpzu ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: https://machinelearnia.com/ ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: https://machinelearnia.com/apprendre-le-machine-learning-en-une-semaine/ ► Téléchargez gratuitement mes codes sur github: https://github.com/MachineLearnia ► Abonnez-vous : https://www.youtube.com/channel/UCmpptkXu8iIFe6kfDK5o7VQ ► 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]

How To Think Like A Computer Scientist Learning Python

Python For Computer Vision With Open CV And Deep Learning

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]

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

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.

Bayesian Machine Learning In Python AB Testing

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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

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.

Practical Machine Learning By Example In Python

Practical Machine Learning By Example In Python

Supervised Machine Learning with Python. Classification: Support Vector Machines

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

Want to master Machine Learning in Python? In this video, we break down the Top 10 Python Libraries every ML & AI practitioner should know! These libraries are essential for data preprocessing, model building, deep learning, and performance optimization. 🔹 Scikit-Learn – The go-to library for ML models 🤖 🔹 TensorFlow & PyTorch – Powering Deep Learning & Neural Networks 🔥 🔹 XGBoost & LightGBM – Boosting algorithms for high-performance ML 📈 🔹 Pandas & NumPy – Data manipulation & numerical computing 📊 🔹 Matplotlib & Seaborn – Data visualization & insights 📉 🔹 NLTK & SpaCy – NLP & text analytics 📝 ✅ Want to become a Data Science Expert? Explore the best Data Science Certification Course in Delhi and take your ML skills to the next level! For more information visit our website: https://bostoninstituteofanalytics.org/india/delhi/connaught-place/school-of-technology-ai/data-science-and-artificial-intelligence/

Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.

Learning Python . 4th Ed. Mark Lutz. 2009. O'Reilly Part 1: getting started Part 2: types and operations Part 3: statements and syntax Part 4: functions Part 5: modules Part 6: classes and OOP Part 7: exceptions and tools Part 8: advanced topics sha256: 585922f4ad0178084035914fd20df932a0662c6de97c468e248201cb46f53874

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