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Learning Python

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1[LinkedInx Learning] - Técnicas Avançadas De Python

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  • Title: ➤  [LinkedInx Learning] - Técnicas Avançadas De Python

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The book is available for download in "data" format, the size of the file-s is: 1388.37 Mbs, the file-s for this book were downloaded 12 times, the file-s went public at Thu Nov 11 2021.

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2Machine Learning De A A La Z - R Y Python Para Data Science (2020)(Udemy)

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

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  • Title: ➤  Machine Learning De A A La Z - R Y Python Para Data Science (2020)(Udemy)

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The book is available for download in "movies" format, the size of the file-s is: 30188.24 Mbs, the file-s for this book were downloaded 1974 times, the file-s went public at Tue Jun 15 2021.

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3NASA Technical Reports Server (NTRS) 20220003102: MLtool: Universal Supervised Machine Learning Tool To Model Tabulated Data MLtool Python Code

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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.

“NASA Technical Reports Server (NTRS) 20220003102: MLtool: Universal Supervised Machine Learning Tool To Model Tabulated Data MLtool Python Code” Metadata:

  • Title: ➤  NASA Technical Reports Server (NTRS) 20220003102: MLtool: Universal Supervised Machine Learning Tool To Model Tabulated Data MLtool Python Code
  • Author: ➤  
  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 1.63 Mbs, the file-s for this book were downloaded 18 times, the file-s went public at Tue May 30 2023.

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4[EuroPython 2019] Thomas Kluiters - Securely Executing Python Machine Learning Models With Distroless Images At ING

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/

“[EuroPython 2019] Thomas Kluiters - Securely Executing Python Machine Learning Models With Distroless Images At ING” Metadata:

  • Title: ➤  [EuroPython 2019] Thomas Kluiters - Securely Executing Python Machine Learning Models With Distroless Images At ING
  • Language: English

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The book is available for download in "movies" format, the size of the file-s is: 1833.89 Mbs, the file-s for this book were downloaded 38 times, the file-s went public at Thu Nov 05 2020.

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5Learning Predictive Analytics With Python : Gain Practical Insights Into Predictive Modelling By Implementing Predictive Analytics Algorithms On Public Datasets With Python

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Executing machine learning models in a production environment can be tricky, especially at a major bank where compliance and risk are carefully taken into account. In this talk I explain how, we, at ING (a large bank operating on global scale), execute our Python models in a production environment by building minimal Docker images for python versions. I will first talk about the possible security risks of running any docker container in a production environment. Then I will talk about ways in which we can make Docker containers more secure by building minimal docker images for Python. Finally I will explain how these docker images are used in practice to serve machine learning models at ING. Prerequisites: - Some basic knowledge of Docker can be helpful - Some basic understanding of security can be helpful Goals: - Understand the security risks of running docker containers - Know how to make docker images more secure - How to build secure model serving docker images Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

“Learning Predictive Analytics With Python : Gain Practical Insights Into Predictive Modelling By Implementing Predictive Analytics Algorithms On Public Datasets With Python” Metadata:

  • Title: ➤  Learning Predictive Analytics With Python : Gain Practical Insights Into Predictive Modelling By Implementing Predictive Analytics Algorithms On Public Datasets With Python
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 791.23 Mbs, the file-s for this book were downloaded 103 times, the file-s went public at Mon May 16 2022.

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6Mlpy: Machine Learning Python

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mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3 at the website http://mlpy.fbk.eu.

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  • Title: Mlpy: Machine Learning Python
  • Authors: ➤  

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The book is available for download in "texts" format, the size of the file-s is: 2.67 Mbs, the file-s for this book were downloaded 983 times, the file-s went public at Mon Sep 23 2013.

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7Andrew 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'

“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” Metadata:

  • Title: ➤  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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The book is available for download in "texts" format, the size of the file-s is: 244.39 Mbs, the file-s for this book were downloaded 38 times, the file-s went public at Sat May 04 2024.

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8Learning Python Manual (4th Edition)(Chinese Edition)

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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'

“Learning Python Manual (4th Edition)(Chinese Edition)” Metadata:

  • Title: ➤  Learning Python Manual (4th Edition)(Chinese Edition)
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 1846.56 Mbs, the file-s for this book were downloaded 41 times, the file-s went public at Sun Dec 10 2023.

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9Introduction To Machine Learning With Python

aibot machine learning

“Introduction To Machine Learning With Python” Metadata:

  • Title: ➤  Introduction To Machine Learning With Python
  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 157.63 Mbs, the file-s for this book were downloaded 51 times, the file-s went public at Sat Jan 11 2025.

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10Python Machine Learning

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“Python Machine Learning” Metadata:

  • Title: Python Machine Learning

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The book is available for download in "texts" format, the size of the file-s is: 323.19 Mbs, the file-s for this book were downloaded 29 times, the file-s went public at Tue Mar 04 2025.

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11How To Think Like A Computer Scientist Learning Python

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How To Think Like A Computer Scientist Learning Python

“How To Think Like A Computer Scientist Learning Python” Metadata:

  • Title: ➤  How To Think Like A Computer Scientist Learning Python
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 92.29 Mbs, the file-s for this book were downloaded 49 times, the file-s went public at Wed Oct 04 2023.

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12Python: Learn Python In 24 Hours Or Less - A Beginner’s Guide To Learning Python Programming Now

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How To Think Like A Computer Scientist Learning Python

“Python: Learn Python In 24 Hours Or Less - A Beginner’s Guide To Learning Python Programming Now” Metadata:

  • Title: ➤  Python: Learn Python In 24 Hours Or Less - A Beginner’s Guide To Learning Python Programming Now
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 321.33 Mbs, the file-s for this book were downloaded 48 times, the file-s went public at Wed May 25 2022.

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13Thoughtful Machine Learning With Python : A Test-driven Approach

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How To Think Like A Computer Scientist Learning Python

“Thoughtful Machine Learning With Python : A Test-driven Approach” Metadata:

  • Title: ➤  Thoughtful Machine Learning With Python : A Test-driven Approach
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 531.71 Mbs, the file-s for this book were downloaded 110 times, the file-s went public at Sat May 14 2022.

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14Python Machine Learning Tutorial (Data Science)

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How To Think Like A Computer Scientist Learning Python

“Python Machine Learning Tutorial (Data Science)” Metadata:

  • Title: ➤  Python Machine Learning Tutorial (Data Science)
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The book is available for download in "movies" format, the size of the file-s is: 519.75 Mbs, the file-s for this book were downloaded 197 times, the file-s went public at Fri Apr 05 2024.

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15Python Machine Learning From Scratch : Machine Learning Concepts And Applications For Beginners

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130 pages : 23 cm

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  • Title: ➤  Python Machine Learning From Scratch : Machine Learning Concepts And Applications For Beginners
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 309.76 Mbs, the file-s for this book were downloaded 277 times, the file-s went public at Tue Aug 16 2022.

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16Mastering Git Switch, Python Learning Sites, And Debugging Ruby On Rails

"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/

“Mastering Git Switch, Python Learning Sites, And Debugging Ruby On Rails” Metadata:

  • Title: ➤  Mastering Git Switch, Python Learning Sites, And Debugging Ruby On Rails
  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 2.35 Mbs, the file-s for this book were downloaded 11 times, the file-s went public at Fri Sep 20 2024.

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17มองโลก มองไทย - 'Python' ภาษาเบื้องต้นสำหรับ Machine Learning

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#มองโลกมองไทย ประจำวันที่ 25 ตุลาคม 2563 คุยกับคอมพิวเตอร์ด้วยภาษามนุษย์? ปราบ เลาหะโรจนพันธ์ อาจารย์พิเศษ วิชา Machine Learning ม.ธรรมศาสตร์ แขกรับเชิญพิเศษจะมาเล่าให้ฟังว่า 'ทักษะภาษาคอมพ์ฯ' ช่วยให้เราพลิกวิธีคิด พลิกอนาคตอย่างไร และทำไม โลกถึงนิยม และมองหาคนที่มีทักษะนี้เสียเหลือเกิน? Machine Learning สำหรับมนุษย์เงินเดือน โดย ThinkLab Creative Space & Cafe https://www.facebook.com/thinklab.creativespace/posts/194751758721175 พิเศษ! เพียงระบุว่ารับชมมาจากรายการ "มองโลก มองไทย" รับส่วนลด 10% Facebook Page: ThinkLab Creative Space and Cafe โทรศัพท์/ LINE ID: 0610247199 สมัครสมาชิกเพื่อรับสิทธิพิเศษ (Membership) https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw/join ติดตาม #VoiceTV YouTube : https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw Facebook : https://www.facebook.com/pg/VoiceTVRankingThailand Instagram : https://www.instagram.com/voicetv/ Twitter : https://twitter.com/VoiceTVOfficial Website : https://www.voicetv.co.th/ Source: https://www.youtube.com/watch?v=fUrtterb4Z0 Uploader: VOICE TV

“มองโลก มองไทย - 'Python' ภาษาเบื้องต้นสำหรับ Machine Learning” Metadata:

  • Title: ➤  มองโลก มองไทย - 'Python' ภาษาเบื้องต้นสำหรับ Machine Learning
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The book is available for download in "movies" format, the size of the file-s is: 329.21 Mbs, the file-s for this book were downloaded 50 times, the file-s went public at Mon Oct 26 2020.

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18Mastering Machine Learning With Python In Six Steps : A Practical Implementation Guide To Predictive Data Analytics Using Python

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#มองโลกมองไทย ประจำวันที่ 25 ตุลาคม 2563 คุยกับคอมพิวเตอร์ด้วยภาษามนุษย์? ปราบ เลาหะโรจนพันธ์ อาจารย์พิเศษ วิชา Machine Learning ม.ธรรมศาสตร์ แขกรับเชิญพิเศษจะมาเล่าให้ฟังว่า 'ทักษะภาษาคอมพ์ฯ' ช่วยให้เราพลิกวิธีคิด พลิกอนาคตอย่างไร และทำไม โลกถึงนิยม และมองหาคนที่มีทักษะนี้เสียเหลือเกิน? Machine Learning สำหรับมนุษย์เงินเดือน โดย ThinkLab Creative Space & Cafe https://www.facebook.com/thinklab.creativespace/posts/194751758721175 พิเศษ! เพียงระบุว่ารับชมมาจากรายการ "มองโลก มองไทย" รับส่วนลด 10% Facebook Page: ThinkLab Creative Space and Cafe โทรศัพท์/ LINE ID: 0610247199 สมัครสมาชิกเพื่อรับสิทธิพิเศษ (Membership) https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw/join ติดตาม #VoiceTV YouTube : https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw Facebook : https://www.facebook.com/pg/VoiceTVRankingThailand Instagram : https://www.instagram.com/voicetv/ Twitter : https://twitter.com/VoiceTVOfficial Website : https://www.voicetv.co.th/ Source: https://www.youtube.com/watch?v=fUrtterb4Z0 Uploader: VOICE TV

“Mastering Machine Learning With Python In Six Steps : A Practical Implementation Guide To Predictive Data Analytics Using Python” Metadata:

  • Title: ➤  Mastering Machine Learning With Python In Six Steps : A Practical Implementation Guide To Predictive Data Analytics Using Python
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 1059.46 Mbs, the file-s for this book were downloaded 271 times, the file-s went public at Fri Nov 04 2022.

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19How To Think Like A Computer Scientist Learning With Python

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#มองโลกมองไทย ประจำวันที่ 25 ตุลาคม 2563 คุยกับคอมพิวเตอร์ด้วยภาษามนุษย์? ปราบ เลาหะโรจนพันธ์ อาจารย์พิเศษ วิชา Machine Learning ม.ธรรมศาสตร์ แขกรับเชิญพิเศษจะมาเล่าให้ฟังว่า 'ทักษะภาษาคอมพ์ฯ' ช่วยให้เราพลิกวิธีคิด พลิกอนาคตอย่างไร และทำไม โลกถึงนิยม และมองหาคนที่มีทักษะนี้เสียเหลือเกิน? Machine Learning สำหรับมนุษย์เงินเดือน โดย ThinkLab Creative Space & Cafe https://www.facebook.com/thinklab.creativespace/posts/194751758721175 พิเศษ! เพียงระบุว่ารับชมมาจากรายการ "มองโลก มองไทย" รับส่วนลด 10% Facebook Page: ThinkLab Creative Space and Cafe โทรศัพท์/ LINE ID: 0610247199 สมัครสมาชิกเพื่อรับสิทธิพิเศษ (Membership) https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw/join ติดตาม #VoiceTV YouTube : https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw Facebook : https://www.facebook.com/pg/VoiceTVRankingThailand Instagram : https://www.instagram.com/voicetv/ Twitter : https://twitter.com/VoiceTVOfficial Website : https://www.voicetv.co.th/ Source: https://www.youtube.com/watch?v=fUrtterb4Z0 Uploader: VOICE TV

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20Mastering Git Switch, Python Learning Sites, And Debugging Ruby On Rails

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/

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21Solar Wind In Situ Data Suitable For Machine Learning (python Numpy Structured Arrays): STEREO-A/B, Wind, Parker Solar Probe, Ulysses, Venus Express, MESSENGER

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These are solar wind in situ data arrays in python pickle format suitable for machine learning, i.e. the arrays consist only of numbers, no strings and no datetime objects. See AAREADME_insitu_ML.txt for more explanation. If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin): [email protected] or twitter @chrisoutofspace Made with https://github.com/cmoestl/heliocats Load in python with e.g. for Parker Solar Probe data: > import pickle > filepsp='psp_2018_2019_sceq_ndarray.p' > [psp,hpsp]=pickle.load(open(filepsp, "rb" ) ) plot time vs total field > import matplotlib.pyplot as plt > plt.plot(psp['time'],psp['bt']) Times psp[:,0 ] or psp['time'] are in matplotlib format. Variable 'hpsp' contains a header with the variable names and units for each column. Coordinate systems for magnetic field components are RTN (Ulysses), SCEQ (Parker Solar Probe, STEREO-A/B, VEX, MESSENGER), HEEQ (Wind) available parameters: bt = total magnetic field bxyz = magnetic field components vt = total proton speed vxyz = velocity components (only for PSP) np = proton density tp = proton temperature xyz = spacecraft position in HEEQ r, lat, lon = spherical coordinates of position in HEEQ

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22A Framework For Distributed Deep Learning Layer Design In Python

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In this paper, a framework for testing Deep Neural Network (DNN) design in Python is presented. First, big data, machine learning (ML), and Artificial Neural Networks (ANNs) are discussed to familiarize the reader with the importance of such a system. Next, the benefits and detriments of implementing such a system in Python are presented. Lastly, the specifics of the system are explained, and some experimental results are presented to prove the effectiveness of the system.

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23After Work Data Science Ensemble Learning With Python Project

After Work Data Science Ensemble Learning With Python Project

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24Top 10 Python Libraries For Machine Learning

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/

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25Introduction To Machine Learning With Python

Power of Machine Learning with python

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26Introduction To Machine Learning With Python : A Guide For Data Scientists

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Power of Machine Learning with python

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27PYTHON SKLEARN: KNN, LinearRegression Et SUPERVISED LEARNING (20/30)

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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]

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28Introduction To Machine Learning With Python

21332231322

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29MACHINE LEARNING PYTHON (Teaser)

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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]

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30FORMATION PYTHON MACHINE LEARNING (2020) (1/30)

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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]

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31289+ Machine Learning Projects With Python Code

289+ Machine Learning Projects With Python Code

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32[LinkedInx Learning] - Descubra O Python

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33Python For Artificial Intelligence And Machine Learning

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Python has become the dominant programming language in the fields of Artificial Intelligence AI and Machine Learning ML due to its user friendly nature, versatility, and the vast array of libraries available to developers. This paper explores how Python facilitates the rapid development of AI and ML applications, particularly through popular frameworks such as TensorFlow, PyTorch, and Scikit learn. Additionally, a case study on image classification using Convolutional Neural Networks CNNs demonstrates Pythons practical applications in real world scenarios. The comparative analysis presented in this paper emphasizes Pythons effectiveness in fostering scalable and reproducible research in the field of AI. M. Yamuna "Python for Artificial Intelligence and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd79847.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/79847/python-for-artificial-intelligence-and-machine-learning/m-yamuna

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34Python-Powered Machine Learning In The Cloud

Python has become the dominant programming language in the fields of Artificial Intelligence AI and Machine Learning ML due to its user friendly nature, versatility, and the vast array of libraries available to developers. This paper explores how Python facilitates the rapid development of AI and ML applications, particularly through popular frameworks such as TensorFlow, PyTorch, and Scikit learn. Additionally, a case study on image classification using Convolutional Neural Networks CNNs demonstrates Pythons practical applications in real world scenarios. The comparative analysis presented in this paper emphasizes Pythons effectiveness in fostering scalable and reproducible research in the field of AI. M. Yamuna "Python for Artificial Intelligence and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd79847.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/79847/python-for-artificial-intelligence-and-machine-learning/m-yamuna

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35Machine Learning Engineering Principles With Python And MLFlow

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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

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36Github.com-rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49

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The "Python Machine Learning (2nd edition)" book code repository and info resource Python Machine Learning (2nd Ed.) Code Repository Python Machine Learning, 2nd Ed. published September 20th, 2017 Paperback: 622 pages Publisher: Packt Publishing Language: English ISBN-10: 1787125939 ISBN-13: 978-1787125933 Kindle ASIN: B0742K7HYF Links Amazon Page Packt Page Table of Contents and Code Notebooks Helpful installation and setup instructions can be found in the README.md file of Chapter 1 To access the code materials for a given chapter, simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub. In addition, the code/ subdirectories also contain .py script files, which were created from the Jupyter Notebooks. However, I highly recommend working with the Jupyter notebook if possible in your computing environment. Not only do the Jupyter notebooks contain the images and section headings for easier navigation, but they also allow for a stepwise execution of individual code snippets, which -- in my opinion -- provide a better learning experience. Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text. Machine Learning - Giving Computers the Ability to Learn from Data [[open dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] Training Machine Learning Algorithms for Classification [[open dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] A Tour of Machine Learning Classifiers Using Scikit-Learn [[open dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] Building Good Training Sets – Data Pre-Processing [[open dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] Compressing Data via Dimensionality Reduction [[open dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[open dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] Combining Different Models for Ensemble Learning [[open dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] Applying Machine Learning to Sentiment Analysis [[open dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] Embedding a Machine Learning Model into a Web Application [[open dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] Predicting Continuous Target Variables with Regression Analysis [[open dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] Working with Unlabeled Data – Clustering Analysis [[open dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] Implementing a Multi-layer Artificial Neural Network from Scratch [[open dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] Parallelizing Neural Network Training with TensorFlow [[open dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] Going Deeper: The Mechanics of TensorFlow [[open dir](./code/ch14)] [[ipynb](./code/ch14/ch14.ipynb)] Classifying Images with Deep Convolutional Neural Networks [[open dir](./code/ch15)] [[ipynb](./code/ch15/ch15.ipynb)] Modeling Sequential Data Using Recurrent Neural Networks [[open dir](./code/ch16)] [[ipynb](./code/ch16/ch16.ipynb)] What’s new in the second edition from the first edition? Oh, there are so many things that we improved or added; where should I start!? The one issue on top of my priority list was to fix all the nasty typos that were introduced during the layout stage or my oversight. I really appreciated all the helpful feedback from readers in this manner! Furthermore, I addressed all the feedback about sections that may have been confusing or a bit unclear, reworded paragraphs, and added additional explanations. Also, special thanks go to the excellent editors of the second edition, who helped a lot along the way! Also, the figures and plots became much prettier. While readers liked the graphic content a lot, some people criticized the PowerPoint-esque style and layout. Thus, I decided to overhaul every little figure with a hopefully more pleasing choice of fonts and colors. Also, the data plots look much nicer now, thanks to the matplotlib team who put a lot of work in matplotlib 2.0 and its new styling theme. Beyond all these cosmetic fixes, new sections were added here and there. Among these is, for example, is a section on dealing with imbalanced datasets, which several readers were missing in the first edition and short section on Latent Dirichlet Allocation among others. As time and the software world moved on after the first edition was released in September 2015, we decided to replace the introduction to deep learning via Theano. No worries, we didn't remove it but it got a substantial overhaul and is now based on TensorFlow, which has become a major player in my research toolbox since its open source release by Google in November 2015. Along with the new introduction to deep learning using TensorFlow, the biggest additions to this new edition are three brand new chapters focussing on deep learning applications: A more detailed overview of the TensorFlow mechanics, an introduction to convolutional neural networks for image classification, and an introduction to recurrent neural networks for natural language processing. Of course, and in a similar vein as the rest of the book, these new chapters do not only provide readers with practical instructions and examples but also introduce the fundamental mathematics behind those concepts, which are an essential building block for understanding how deep learning works. [ [Excerpt from "Machine Learning can be useful in almost every problem domain:" An interview with Sebastian Raschka](https://www.packtpub.com/books/content/machine-learning-useful-every-problem-domain-interview-sebastian-raschka/) ] Raschka, Sebastian, and Vahid Mirjalili. Python Machine Learning, 2nd Ed . Packt Publishing, 2017. @book{RaschkaMirjalili2017, address = {Birmingham, UK}, author = {Raschka, Sebastian and Mirjalili, Vahid}, edition = {2}, isbn = {978-1787125933}, keywords = {Clustering,Data Science,Deep Learning, Machine Learning,Neural Networks,Programming, Supervised Learning}, publisher = {Packt Publishing}, title = {{Python Machine Learning, 2nd Ed.}}, year = {2017} } Translations German ISBN-10: 3958457339 ISBN-13: 978-3958457331 Amazon.de link Publisher link Japanese ISBN-10: 4295003379 ISBN-13: 978-4295003373 Amazon.co.jp link To restore the repository download the bundle wget https://archive.org/download/github.com-rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49/rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49.bundle and run: git clone rasbt-python-machine-learning-book-2nd-edition_-_2019-11-15_20-49-49.bundle Source: https://github.com/rasbt/python-machine-learning-book-2nd-edition Uploader: rasbt Upload date: 2019-11-15

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37A Machine Learning Approach In Python Is Used To Forecast The Number Of Train Passengers Using A Fuzzy Time Series Model

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.

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38Udemy Autonomous Cars Deep Learning And Computer Vision In Python

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39Learning To Learn Python

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40TensorLy: Tensor Learning In Python

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

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41Learning Python, 4th Edition

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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

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42Learning Python Design Patterns : Leverage The Power Of Python Design Patterns To Solve Real-world Problems In Software Architecture And Design

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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

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43Building Machine Learning Demos With Python

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By: Omar Sanseviero Event: PyConZA 2021 Url: https://2021.za.pycon.org/talks/37-building-machine-learning-demos-with-python/ About: How can you show what a Machine Learning model does once it's trained? In this talk, you're going to learn how to create Machine Learning apps and demos using Streamlit and Gradio, Python libraries for this purpose. Additionally, you'll see how to share them with the rest of the Open Source ecosystem. Learning to create graphic interfaces for models is extremely useful for sharing with other people interesting with them. Room: Video Room 2 Scheduled start: 2021-10-07 13:45:00 Sponsors: Gold: SPAN Digital: https://spandigital.com/ Takealot: http://takealot.com/ Andela: https://www.andela.com/ Silver: Python Software Foundation: https://www.python.org/psf/membership OfferZen: https://www.offerzen.com/ Patron: Thinkst Canary: https://canary.tools/ Afrolabs: http://www.afrolabs.co.za/

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4423, Python And Me: Using Machine Learning In Python To Analyze Consumer Genomics Data

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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

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45Python Learning Manual - Version 3

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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

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46PYTHON NUMPY Machine Learning (10/30)

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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]

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47Deployment Of Machine Learning Models In Production Python

Deployment of Machine Learning Models in Production Python

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48[EuroPython 2020] V. Fedotova/F. Schlimbach - The Painless Route In Python To Fast And Scalable Machine Learning

Python is the lingua franca for data analytics and machine learning. Its superior productivity makes it the preferred tool for prototyping. However, traditional Python packages are not necessarily designed to provide high performance and scalability for large datasets. From this talk you will learn how to get close-to-native performance with Intel-optimized packages, such as numpy, scipy, and scikit-learn. The next part of the talk is focused on getting high performance and scalability from multi-cores on a single machine to large clusters of workstations. It will be demonstrated that with Python it is possible to achieve the same performance and scalability as with hand-tuned C++/MPI code: - Scalable Dataframe Compiler (SDC) makes possible to efficiently load and process huge datasets using pandas/Python. - A convenient Python API to data analytics and machine learning primitives (daal4py). While its interface is scikit-learn-like, its MPI-based engine allows to scale machine learning algorithms to bare-metal cluster performance. - From the talk you will learn how to use SDC and daal4py together to build an end-to-end analytics pipeline that scales to clusters, requiring only minimal code changes. Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/

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49Introduction To Machine Learning With Python ( PDFDrive.com )

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50Introduction To Machine Learning With Python ( PDFDrive.com )

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1Learning Python

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“Learning Python” Metadata:

  • Title: Learning Python
  • Authors:
  • Languages: English - ger
  • Number of Pages: Median: 591
  • Publisher: ➤  O'Reilly Media - O'Reilly Media, Incorporated - O'Reilly - Shroff Publishers & Distributors Pvt. Ltd. - CreateSpace Independent Publishing Platform - O'Reilly Media, Inc.
  • Publish Date: ➤  
  • Publish Location: ➤  Sebstopol - Sebastopol, CA - Cambridge - Taipei - Tokyo - Farnham - Paris - Köln - Beijing - CA 95472

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First Setence:

"Dieses Buch bietet Ihnen eine kurze Einführung in die Programmiersprache Python."

Access and General Info:

  • First Year Published: 1999
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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2Learn to Program Using Python

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“Learn to Program Using Python” Metadata:

  • Title: Learn to Program Using Python
  • Author:
  • Language: English
  • Number of Pages: Median: 279
  • Publisher: ➤  Addison-Wesley - Addison-Wesley Professional
  • Publish Date:
  • Publish Location: Reading, MA

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Access and General Info:

  • First Year Published: 2000
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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3Python machine learning from scratch

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“Python machine learning from scratch” Metadata:

  • Title: ➤  Python machine learning from scratch
  • Author:
  • Language: English
  • Number of Pages: Median: 130
  • Publisher: AI Sciences
  • Publish Date:
  • Publish Location: Lewis, Delware

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Access and General Info:

  • First Year Published: 2016
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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4Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python

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  • Title: ➤  Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python
  • Author:
  • Number of Pages: Median: 358
  • Publisher: Apress
  • Publish Date:

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Access and General Info:

  • First Year Published: 2017
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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5Reinforcement learning with Python

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“Reinforcement learning with Python” Metadata:

  • Title: ➤  Reinforcement learning with Python
  • Author:
  • Language: English
  • Number of Pages: Median: 48
  • Publisher: ➤  [CreateSpace Independent Publishing Platform]
  • Publish Date:
  • Publish Location: United States]

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  • First Year Published: 2017
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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6Everything I ever needed to know about _____* I learned from Monty Python

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  • Title: ➤  Everything I ever needed to know about _____* I learned from Monty Python
  • Author:
  • Language: English
  • Number of Pages: Median: 320
  • Publisher: St. Martin's Press
  • Publish Date:

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Access and General Info:

  • First Year Published: 2014
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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7Learn Raspberry Pi Programming with Python

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“Learn Raspberry Pi Programming with Python” Metadata:

  • Title: ➤  Learn Raspberry Pi Programming with Python
  • Author:
  • Language: English
  • Number of Pages: Median: 256
  • Publisher: ➤  Apress - Distributed to the book trade worldwide by Springer Science+Business Media New York - Apress L. P.
  • Publish Date:

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  • First Year Published: 2014
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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8Learn to Program with Python

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“Learn to Program with Python” Metadata:

  • Title: Learn to Program with Python
  • Author:
  • Number of Pages: Median: 276
  • Publisher: Apress
  • Publish Date:

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  • First Year Published: 2016
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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9Python learning Manual - Version 3

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“Python learning Manual - Version 3” Metadata:

  • Title: ➤  Python learning Manual - Version 3
  • Author: ➤  
  • Publisher: ➤  Mechanical Industry Press Pub. Date :2009-08
  • Publish Date:

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  • First Year Published: 2000
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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10Learn Coding Basics in Hours with Python

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“Learn Coding Basics in Hours with Python” Metadata:

  • Title: ➤  Learn Coding Basics in Hours with Python
  • Authors:
  • Number of Pages: Median: 108
  • Publisher: Jack Stanley
  • Publish Date:

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Access and General Info:

  • First Year Published: 2017
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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11Learning Python Manual (4th edition)(Chinese Edition)

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  • Title: ➤  Learning Python Manual (4th edition)(Chinese Edition)
  • Author:
  • Publisher: Machinery Industry Press
  • Publish Date:

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Access and General Info:

  • First Year Published: 2011
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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