Python 3: from None to Machine Learning

author	Matt Harasymczuk
email	matt@astrotech.io
www	http://www.astrotech.io
github	https://github.com/astromatt
linkedin	https://linkedin.com/in/mattharasymczuk
facebook	https://facebook.com/matt.harasymczuk
slideshare	https://www.slideshare.net/astrotech/presentations

Tip

• This book is free and open source at http://python.astrotech.io

• For consulting or training course requests please email matt@astrotech.io

Table 1. Other books from author

URL	Title
http://python.astrotech.io	Python 3: from None to Machine Learning
http://dev.astrotech.io	DevOps and CI/CD with Docker
http://jira.astrotech.io	Jira and Atlassian Ecosystem
http://git.astrotech.io	Git and Git Flow in CI/CD
http://agile.astrotech.io	Agile Software Engineering: CI/CD with Scrum, Kanban, XP and Lean
http://arch.astrotech.io	Software Architecture, Cloud, Microservices
http://linux.astrotech.io	Linux and Shell Programming with Bash

Copyright

MIT License

Copyright (c) 2019 Matt Harasymczuk

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Workshop

Warning

Before training course please setup your environment as described here Installing Python.

• Python Foundations

• Python Intermediate

• Python Advanced

• Django Framework

• Python in Networking

• Python in Testing

• Python in Science and Engineering

• Python in Machine Learning

Introduction

Introduction

• 1. Installing Python
• 2. References in the book
• 3. About Python Language
• 4. IDE - Integrated Development Environment

Python Basics

Python Basics

• 1. Entry Test
• 2. Syntax
• 3. Types
• 4. Sequences
• 5. Mappings
• 6. Conditionals
• 7. Looping
• 8. Control Flow
• 9. Functions
• 10. Object Oriented Programming
• 11. Exit Test

Python Intermediate

Python Intermediate

• 1. Entry Test
• 2. Builtins
• 3. Looping
• 4. Object Oriented Programming
• 5. Modules and Packages
• 6. Mathematics
• 7. Datetime and Timezones
• 8. Serialization
• 9. Database
• 10. Regular Expressions
• 11. Operating System
• 12. GUI
• 13. Exit Test

Python Advanced

Python Advanced

• 1. Entry Test
• 2. Functions
• 3. Performance
• 4. OOP Paradigm
• 5. OOP Protocols
• 6. Object Oriented Programming
• 7. Design Patterns
• 8. Paradigms
• 9. Concurrency
• 10. Exit Test

Quality and CI/CD

Quality and CI/CD

• 1. Software Engineering Conventions
• 2. The Zen of Python
• 3. Code Smells
• 4. ReST and Sphinx documentation
• 5. Unit Testing
• 6. Pytest
• 7. Python WAT?!
• 8. Logging
• 9. Warnings
• 10. Basic Debugging
• 11. Advanced Debugging
• 12. Introspection
• 13. Type Annotation
• 14. Introspection
• 15. Type Checking
• 16. Annotating existing code
• 17. CI/CD

Network and HTTP

Network and HTTP

• 1. Protocols
  • 1.1. FTP
  • 1.2. IMAP
  • 1.3. SMTP
  • 1.4. POP3
  • 1.5. LDAP
  • 1.6. SSH
  • 1.7. DNS
• 2. Transport
  • 2.1. OSI Model
  • 2.2. Interfaces
  • 2.3. Routing
  • 2.4. Socket
  • 2.5. SSL
  • 2.6. IPv4/IPv6
  • 2.7. TCP/UDP
  • 2.8. scapy
  • 2.9. 3rd Party Libraries
  • 2.10. Mini Botnet
• 3. HTTP and Web
  • 3.1. HTTP Protocol
  • 3.2. HTTP using stdlib
  • 3.3. HTTP using requests
  • 3.4. HTML Scrapping
  • 3.5. HTTP Frameworks
  • 3.6. Templates with Jinja2
  • 3.7. PaaS - Platform as a Service
  • 3.8. SOAP and WSDL
  • 3.9. Asynchronous processing
• 4. Django
  • 4.1. HTTP
  • 4.2. Introduction to Django
  • 4.3. Django Framework Architecture
  • 4.4. Installation and Running
  • 4.5. Settings
  • 4.6. Apps
  • 4.7. Models
  • 4.8. URL Router
  • 4.9. ORM
  • 4.10. Views
  • 4.11. Forms
  • 4.12. REST
  • 4.13. Staticfiles
  • 4.14. Templates
  • 4.15. Templatetags
  • 4.16. Admin panel
  • 4.17. Management Commands
  • 4.18. i18n and l10n
  • 4.19. Database
  • 4.20. Cache
  • 4.21. Signals
  • 4.22. Auth and Permissions
  • 4.23. API CORS
  • 4.24. Standalone scripts
  • 4.25. Tests and quality
  • 4.26. Tests
  • 4.27. CI/CD - Continuous Integration and Deployment
  • 4.28. Deployment

Numerical Analysis, Data Science

Numerical Analysis, Data Science

• 1. Introduction
• 2. Jupyter
• 3. Python
• 4. Numpy
• 5. Pandas
• 6. Matplotlib
• 7. Data Visualization
• 8. Scipy

Machine Learning

Machine Learning

• 1. Introduction
• 2. Sklearn
• 3. Model Quality
• 4. Decision Trees
• 5. Regressions
• 6. K-Nearest Neighbors
• 7. Bayes
• 8. Support Vector Machines
• 9. Clustering
• 10. Neural Networks
• 11. References

Blogposts

Blogposts

• 1. Wprowadzenie do Machine Learning w Pythonie
  • 1.1. Czym jest uczenie maszynowe?
  • 1.2. Co trzeba wiedzieć przed rozpoczęciem pracy?
  • 1.3. Ważne pytania przed przystąpieniem do tworzenia algorytmu
  • 1.4. Czyszczenie Danych
  • 1.5. Praca z bibliotekami
  • 1.6. Kategorie algorytmów uczenia maszynowego

Appendixes

Appendixes

• 1. History
  • 1.1. BDFL - Guido van Rossum
  • 1.2. Name
  • 1.3. PSF - Python Software Foundation
• 2. Further reading
  • 2.1. My favourite sources
  • 2.2. Documentation
  • 2.3. Working with Excel
  • 2.4. IDE - Integrated Development Environment
  • 2.5. Others
  • 2.6. IT Fun
  • 2.7. Wyzwania i potyczki
• 3. Books
  • 3.1. Algorithms
  • 3.2. Databases
  • 3.3. Software Engineering Practises
  • 3.4. Design patterns
  • 3.5. Refactoring
  • 3.6. Python
• 4. Video
  • 4.1. Collections of videos
  • 4.2. Python
  • 4.3. Machine Learning and Data Science
  • 4.4. Filmy z ostatnich konferencji
• 5. Python 2 vs. 3
  • 5.1. six
  • 5.2. Lista kompatybilności Python 2 i Python 3
  • 5.3. Unicode i kodowanie znaków
  • 5.4. Biblioteki standardowe w 2 i 3
• 6. Python Foundations
  • 6.1. Description
  • 6.2. Agenda
  • 6.3. Survey before workshop
  • 6.4. Certificate
• 7. Python Intermediate
  • 7.1. Description
  • 7.2. Agenda
  • 7.3. Survey before workshop
  • 7.4. Certificate
• 8. Python Advanced
  • 8.1. Description
  • 8.2. Agenda
  • 8.3. Survey before workshop
  • 8.4. Certificate
• 9. Python in Networking
  • 9.1. Description
  • 9.2. Agenda
  • 9.3. Survey before workshop
  • 9.4. Certificate
• 10. Django Framework
  • 10.1. Description
  • 10.2. Agenda
  • 10.3. Survey before workshop
  • 10.4. Certificate
• 11. Python in Testing
  • 11.1. Description
  • 11.2. Agenda
  • 11.3. Survey before workshop
  • 11.4. Certificate
• 12. Python in Science and Engineering
  • 12.1. Description
  • 12.2. Agenda
  • 12.3. Survey before workshop
  • 12.4. Certificate
• 13. Python in Machine Learning
  • 13.1. Description
  • 13.2. Agenda
  • 13.3. Survey before workshop
  • 13.4. Certificate
• 14. References
  • 14.1. Bibliography
  • 14.2. Images
  • 14.3. Video
• 15. Copyright
  • 15.1. MIT License