Downloads & Free Reading Options - Results

Power of Machine Learning with python

Power of Machine Learning with python

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 (18 - Support Vector Machine (SVM))

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (05 - Part 2 Regression)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (23 - Classification Model Selection in Python)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (36 - Artificial Neural Networks)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (39 - Principal Component Analysis (PCA))

How To Think Like A Computer Scientist Learning Python

How To Think Like A Computer Scientist Learning 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'

Python For Computer Vision With Open CV And Deep Learning

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

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

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

"The Complete Python, Machine Learning, AI Mega Bundle is a comprehensive collection of courses and resources covering Python programming, machine learning, and artificial intelligence. It is designed to guide learners from beginner to advanced levels, encompassing Python fundamentals, data manipulation, statistical analysis, machine learning algorithms, deep learning, and natural language processing."

This week I review resources aimed at teaching you Python

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

Course in Machine Learning and Data Science

My library

books

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'

My books collection

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

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.

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

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

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (04 - Data Preprocessing in R)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (21 - Decision Tree Classification)

udemy course

ML - python

Machine learning

Machine learning

[ Reading] After Work Data Science Ensemble Learning With Python

autonomus

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

pages cm

Python For Data Science And Machine Learning Bootcamp

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

.

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 (29 - Apriori)

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

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.

Bayesian Machine Learning In Python AB Testing

deep learing

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