Python 3: From None to Machine Learning

Original Title

Python 3: from None to Machine Learning

English Title

Python 3: from None to Machine Learning

Author

Matt Harasymczuk

Language

English

ISBN

978-83-957186-2-5

Year

2015-2020

Online Access

https://python.astrotech.io

License

Creative Commons Attribution-ShareAlike 4.0 International License

Author

author	Matt Harasymczuk
email	book-python@astronaut.center
www	https://www.astronaut.center
github	https://github.com/astromatt
linkedin	https://linkedin.com/in/mattharasymczuk
facebook	https://facebook.com/matt.harasymczuk
slideshare	https://www.slideshare.net/astrotech/presentations

Table 1. Other Books from Author. More information at https://www.astronaut.center/books

ISBN	Online Access	Title
9788395718625	https://python.astrotech.io	Python 3: from None to Machine Learning
9788395718632	https://dev.astrotech.io	The Software Engineering: DevOps, CI/CD, Docker, Provisioning and Git Flow
9788395718601	https://www.astronaut.pl	Astronaut Selection and Training for Long Duration Spaceflight and Extraterrestrial Activity
9788395675201	https://www.astronaut.center/books	Space in Practice: How to Prepare and Conduct Stratospheric Balloon Mission
9788395718649	https://pl.habitatos.space	HabitatOS - Development of operating system prototype for Lunar and Martian habitats
9788395718618	https://alsep.astronaut.center	Geophysics experiments from Apollo Lunar Surface Experiments Package

Note

• For consulting or training course requests please email book-python@astronaut.center

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

About

• License
• Book Writing Progress
• Python Install
• Survey
• References in the Book

Python Basics

Python Basics

• 1. About
  • 1.1. Setup
  • 1.2. Agenda
  • 1.3. Development Environment
  • 1.4. Python Language
  • 1.5. Python Syntax
  • 1.6. Operators
• 2. Types
  • 2.1. Type Int
  • 2.2. Type Float
  • 2.3. Type Bool
  • 2.4. Type None
  • 2.5. Type Str
  • 2.6. Type Str Methods
  • 2.7. Type Annotation
• 3. Sequences
  • 3.1. Sequence List
  • 3.2. Sequence Tuple
  • 3.3. Sequence Set
  • 3.4. Sequence Frozenset
  • 3.5. Sequence Nested
  • 3.6. Sequence GetItem
  • 3.7. Sequence Slice
  • 3.8. Unpacking Assignment
  • 3.9. Sequence Type Annotation
• 4. Mappings
  • 4.1. Mapping Dict
  • 4.2. Mapping Generate
  • 4.3. Mapping Nested
  • 4.4. Mapping Type Annotation
• 5. Control Flow
  • 5.1. Conditional Operators
  • 5.2. Membership
  • 5.3. Conditional Statements
  • 5.4. Conditional Expressions
  • 5.5. Exceptions
• 6. Loops
  • 6.1. Loop While
  • 6.2. Loop For
  • 6.3. Loop Nested Sequences
  • 6.4. Loop Unpacking Assignment
  • 6.5. Recap
  • 6.6. List of Pairs
  • 6.7. List of Tuples
  • 6.8. Unpacking Assignment
  • 6.9. Unused Values
  • 6.10. Mixed
  • 6.11. Enumerate
  • 6.12. Zip
  • 6.13. Assignments
  • 6.14. Loop over Dict
  • 6.15. Loop Comprehension
• 7. Files
  • 7.1. File Path
  • 7.2. File Modes
  • 7.3. File Write
  • 7.4. File Append
  • 7.5. File Read
  • 7.6. File Encoding
  • 7.7. Files Binary
• 8. Functions
  • 8.1. Function Definition
  • 8.2. Function Return
  • 8.3. Function Parameters
  • 8.4. Function Arguments
  • 8.5. Function Scope
  • 8.6. Function Recurrence
  • 8.7. Function Doctest
  • 8.8. Function Generators
  • 8.9. Function Lambda
  • 8.10. Function Type Annotation
• 9. Object Oriented Programming
  • 9.1. OOP Classes and Instances
  • 9.2. OOP Methods
  • 9.3. OOP Attributes
  • 9.4. OOP Init Method
  • 9.5. OOP Methods and Attributes
  • 9.6. OOP Inheritance
  • 9.7. OOP Stringify Objects
  • 9.8. OOP Architecture
  • 9.9. OOP Type Annotation
• 10. Recap
  • 10.1. Good Practices
  • 10.2. Further reading
  • 10.3. Certificate

Python Standard Library

Python Standard Library

• 1. About
  • 1.1. Setup
  • 1.2. Agenda
  • 1.3. Entry Test
• 2. Datetime and Timezones
  • 2.1. Datetime Dates and Time
  • 2.2. Datetime Parsing and Formatting
  • 2.3. Datetime Time Deltas
  • 2.4. Datetime Timestamp
  • 2.5. Datetime Time Utils
  • 2.6. Datetime Timezone
• 3. Serialization
  • 3.1. Serialization CSV
  • 3.2. Serialization JSON
  • 3.3. Serialization Pickle
  • 3.4. Serialization XML, XSLT, XPath
• 4. Database
  • 4.1. Database API
  • 4.2. Database SQLite
  • 4.3. Database MySQL
  • 4.4. Database PostgreSQL
  • 4.5. Database MongoDB
  • 4.6. Database ORM
  • 4.7. Database Schema Migration
  • 4.8. Database Exploration
  • 4.9. Database SQL Injection
  • 4.10. Database SQL
• 5. Regular Expressions
  • 5.1. Regexp Syntax
  • 5.2. Regexp Match
  • 5.3. Regexp Search
  • 5.4. Regexp Findall, Finditer
  • 5.5. Regexp Compare
  • 5.6. Regexp Compile
  • 5.7. Regexp Groups
  • 5.8. Regexp Multiline
  • 5.9. Regexp Substitute
  • 5.10. Regexp Split
  • 5.11. Regexp Non-Greedy
  • 5.12. Regexp Standards
  • 5.13. References
• 6. Modules and Packages
  • 6.1. Module venv
  • 6.2. Module Install
  • 6.3. Module Import
  • 6.4. Tools
  • 6.5. Standalone Applications
  • 6.6. Distributing Packages
• 7. Builtins
  • 7.1. Builtin Functions
  • 7.2. Builtin Keywords
  • 7.3. Operator Library
  • 7.4. Builtin Printing
• 8. Locale
  • 8.1. Locale About
  • 8.2. Locale i18n
  • 8.3. Locale About
• 9. Looping
  • 9.1. Comprehensions Nesting
  • 9.2. Loop else
• 10. Type Annotations
  • 10.1. Type Annotations
  • 10.2. Variables
  • 10.3. Sequences
  • 10.4. Mapping
  • 10.5. Special
  • 10.6. Classes
  • 10.7. Functions and Methods
  • 10.8. Type Aliases
  • 10.9. Introspection
  • 10.10. Type Checking
  • 10.11. Annotating Existing Code
• 11. Object Oriented Programming
  • 11.1. OOP Enum
  • 11.2. OOP Dataclass
• 12. Mathematics
  • 12.1. Math Stdlib
  • 12.2. Math Statistics
  • 12.3. Math Random
  • 12.4. Math Fractions
  • 12.5. Math Precision
  • 12.6. Math Plotting
  • 12.7. Math Complex Numbers
• 13. Operating System
  • 13.1. Tempfile
  • 13.2. Interpreter declaration
  • 13.3. Commandline Arguments
  • 13.4. Operating System
  • 13.5. Pathlib
• 14. GUI
  • 14.1. GUI
• 15. References
  • 15.1. Bibliography

Python Advanced

Python Advanced

• 1. About
  • 1.1. Setup
  • 1.2. Entry Test
  • 1.3. Agenda
• 2. Annotation
  • 2.1. Type Annotation
  • 2.2. Annotation Sequence
  • 2.3. Annotation Mapping
  • 2.4. Function Type Annotation
  • 2.5. Annotation OOP
• 3. Unpacking
  • 3.1. Unpacking Assignment
  • 3.2. Unpacking Parameters
  • 3.3. Unpacking Arguments
  • 3.4. Assignment Expression
• 4. Functions
  • 4.1. Parameter Syntax
  • 4.2. Generators
  • 4.3. Namespace
  • 4.4. Functional Paradigm
  • 4.5. Callable
  • 4.6. Closures
• 5. Decorators
  • 5.1. Decorators About
  • 5.2. Decorator Function with Func
  • 5.3. Decorator Function with Cls
  • 5.4. Decorator Function with Mth
  • 5.5. Decorator Class with Func
  • 5.6. Decorator Class with Cls
  • 5.7. Decorator Class with Mth
  • 5.8. Decorator Arguments
  • 5.9. Decorator Stdlib Functools
  • 5.10. Decorators Recap
• 6. Object Oriented Programming
  • 6.1. OOP Principles
  • 6.2. OOP Mutability
  • 6.3. OOP Dataclass
  • 6.4. Static and Dynamic
  • 6.5. Access
  • 6.6. Overload
  • 6.7. Inheritance vs. Composition
  • 6.8. Polymorphism
  • 6.9. Relations
  • 6.10. Abstract Class
  • 6.11. Object Identity
  • 6.12. Constructor
  • 6.13. S.O.L.I.D.
• 7. Protocols
  • 7.1. Iterator
  • 7.2. Context Manager
  • 7.3. Staticmethod
  • 7.4. Classmethod
  • 7.5. Property
  • 7.6. Reflection
  • 7.7. Descriptor
• 8. Performance
  • 8.1. Performance Optimization
  • 8.2. Micro-benchmarking
  • 8.3. Profiling
  • 8.4. Compilers and Interpreters
  • 8.5. Collections
  • 8.6. C Types
  • 8.7. C Modules
• 9. Concurrency
  • 9.1. Concurrency Models
  • 9.2. Queues
  • 9.3. Threading
  • 9.4. Multiprocessing
  • 9.5. Async Programming
  • 9.6. AsyncIO
• 10. Recap
  • 10.2. Further reading
  • 10.1. Recap
  • 10.2. Certificate

Dragon

Dragon

• 1. Dragon (version alpha)
• 2. Dragon (version beta)
• 3. Dragon (version release candidate)

Numpy

Numpy

• 1. About
  • 1.1. Numpy
  • 1.2. Precision
  • 1.3. Built-ins
• 2. Array
  • 2.1. Array Create
  • 2.2. Array Data Types
  • 2.3. Array Shape
  • 2.4. Array Attributes
  • 2.5. Array Getitem
  • 2.6. Array Slice
  • 2.7. Array Axis
  • 2.8. Array New Axis
  • 2.9. Array Random
  • 2.10. Array Rounding
  • 2.11. Array Sort
  • 2.12. Array Methods
  • 2.13. Array Concatenation
• 3. Select
  • 3.1. Array Iteration
  • 3.2. Array Arithmetic
  • 3.3. Array Broadcasting
  • 3.4. Array Reduction
  • 3.5. Array Logic
  • 3.6. Array Select
  • 3.7. Advanced Indexing
• 4. Import & Export
  • 4.1. Array Serialize
  • 4.2. Data Import and Export
• 5. Math
  • 5.1. Statistics
  • 5.2. Trigonometry
  • 5.3. Polynomials
  • 5.4. Linear Algebra
  • 5.5. Performance

Pandas

Pandas

• 1. About
  • 1.1. Pandas
  • 1.2. Types Basics
• 2. Import & Export
  • 2.1. Pandas Read
  • 2.2. DataFrame Export
• 3. Series
  • 3.1. Series Create
  • 3.2. Series Attributes
  • 3.3. Series Index
  • 3.4. Series Sample
  • 3.5. Series Getitem
  • 3.6. Series Slicing
  • 3.7. Series NA
  • 3.8. Series Alter
  • 3.9. Series Sort
  • 3.10. Series Arithmetic
  • 3.11. Series Statistics
  • 3.12. Series Mapping
• 4. DataFrame
  • 4.1. DataFrame Create
  • 4.2. DataFrame Attributes
  • 4.3. DataFrame Index
  • 4.4. DataFrame Sample
  • 4.5. DataFrame Getitem
  • 4.6. DataFrame Slice
  • 4.7. DataFrame At
  • 4.8. DataFrame Loc
  • 4.9. DataFrame Select
  • 4.10. DataFrame Query
  • 4.11. DataFrame Update
  • 4.12. DataFrame Alter
  • 4.13. DataFrame NA
  • 4.14. DataFrame Sort
  • 4.15. DataFrame Statistics
  • 4.16. DataFrame Mapping
  • 4.17. DataFrame Group By
  • 4.18. DataFrame Aggregations
  • 4.19. DataFrame Join
  • 4.20. DataFrame Plotting
• 5. Date
  • 5.1. Date and Time Types
  • 5.2. Date and Time Timezones
  • 5.3. Date and Time Shifts
  • 5.4. Date and Time Frequency
  • 5.5. Date and Time Calendar
• 6. Recap
  • 6.1. Method Chaining
  • 6.2. Pandas Set Option
  • 6.3. Pandas Workflow
• 7. Case Studies
  • 7.1. Case Study COVID-19

Matplotlib

Matplotlib

• 1. About
  • 1.1. Matplotlib About
• 2. Figure
  • 2.1. Matplotlib Plots
  • 2.2. Matplotlib Subplots
  • 2.3. Matplotlib Style
  • 2.4. Matplotlib Scales
  • 2.5. Matplotlib Config
  • 2.6. Interactive
  • 2.7. Import/Export
• 3. Style
  • 3.1. How to understand charts?
  • 3.2. Plot Styles
• 4. Chart
  • 4.1. Line Chart
  • 4.2. Scatter Plot
  • 4.3. Bar Chart
  • 4.4. Histogram
  • 4.5. Box Chart
  • 4.6. Stacked Plot
  • 4.7. Pie Chart
  • 4.8. 3D Charts
• 5. Advanced
  • 5.1. Advanced Styling
  • 5.2. Basic customizations
  • 5.3. Multiple figures and plots
  • 5.4. Animation
• 6. Recap
  • 6.1. Matplotlib Examples
  • 6.2. Matplotlib Plot Lifecycle
  • 6.3. Case Studies
  • 6.4. Assignments

Object Oriented Programming

Object Oriented Programming

• 1. Paradigm
  • 1.1. Polymorphism
  • 1.2. Interface
  • 1.3. String Interning
  • 1.4. Good Engineering Practises
• 2. Python
  • 2.1. Method Resolution Order
  • 2.2. Namespace
  • 2.3. New and Old Style Class
  • 2.4. Hash
  • 2.5. Slots
  • 2.6. Metaclass
• 3. Dynamic Typing
  • 3.1. Dynamic Typing
  • 3.2. Monkey Patching

Design Patterns

Design Patterns

• 1. About
  • 1.1. Setup
  • 1.2. Entry Test
  • 1.3. Classification
• 2. Annotation
  • 2.1. Type Annotation
  • 2.2. Annotation Sequence
  • 2.3. Annotation Mapping
  • 2.4. Function Type Annotation
  • 2.5. Annotation OOP
• 3. UML
  • 3.1. UML About
  • 3.2. Mermaid
  • 3.3. UML Relationship
  • 3.4. UML Class Diagram
  • 3.5. UML Sequence Diagram
• 4. OOP
  • 4.1. OOP Principles
  • 4.2. OOP Dataclass
  • 4.3. Access
  • 4.4. Overload
  • 4.5. Inheritance vs. Composition
  • 4.6. Interface
  • 4.7. Abstract Class
  • 4.8. Polymorphism
  • 4.9. Dependency Injection
  • 4.10. Assignments
  • 4.11. Metaclass
  • 4.12. S.O.L.I.D.
  • 4.13. Assignments
  • 4.14. Good Engineering Practises
• 5. Idioms
  • 5.1. Loop Comprehension
  • 5.2. Assignment Expression
  • 5.3. Range
  • 5.4. Enumerate
  • 5.5. Zip
  • 5.6. Map
  • 5.7. Filter
  • 5.8. Reduce
  • 5.9. Generators
  • 5.10. Patterns
• 6. Protocols
  • 6.1. Iterator
  • 6.2. Context Manager
  • 6.3. Staticmethod
  • 6.4. Classmethod
  • 6.5. Property
  • 6.6. Reflection
  • 6.7. Descriptor
• 7. Decorators
  • 7.1. Decorators About
  • 7.2. Decorator Function with Func
  • 7.3. Decorator Function with Cls
  • 7.4. Decorator Function with Mth
  • 7.5. Decorator Class with Func
  • 7.6. Decorator Arguments
  • 7.7. Decorator Stdlib Functools
  • 7.8. Decorators Recap
• 8. Behavioral
  • 8.1. Memento
  • 8.2. State
  • 8.3. Iterator
  • 8.4. Strategy
  • 8.5. Template Method
  • 8.6. Command
  • 8.7. Observer
  • 8.8. Mediator
  • 8.9. Chain of Responsibility
  • 8.10. Visitor
  • 8.11. Gateway
  • 8.12. Interpreter
  • 8.13. State Machine
• 9. Structural
  • 9.1. Composite
  • 9.2. Adapter
  • 9.3. Decorator
  • 9.4. Façade
  • 9.5. Flyweight
  • 9.6. Bridge
  • 9.7. Proxy
• 10. Creational
  • 10.1. Singleton
  • 10.2. Borg
  • 10.3. Factory Method
• 11. Practices
• 12. Paradigms
  • 12.1. Callback

DevSecOps

DevSecOps

• 1. Good Engineering Practices
  • 1.1. Software Engineering Conventions
  • 1.2. The Zen of Python
  • 1.3. Code Smells
  • 1.4. ReST and Sphinx documentation
• 2. Tests
  • 2.1. Unit Testing
  • 2.2. Pytest
• 3. Debugging
  • 3.1. Python WAT?!
  • 3.2. Logging
  • 3.3. Warnings
  • 3.4. Basic Debugging
  • 3.5. Advanced Debugging
  • 3.6. Introspection
• 4. Type Annotation
  • 4.1. Type Checking
  • 4.2. Annotating existing code
  • 4.3. Introspection
• 5. CI/CD
  • 5.1. CI/CD Devtools Ecosystem
  • 5.2. CI/CD Tools
  • 5.3. CI/CD Pipelines
  • 5.4. Linters
  • 5.5. Behavioral Testing
  • 5.6. Code Style
  • 5.7. Code Coverage
  • 5.8. Internationalization
  • 5.9. DB Schema Migration
  • 5.10. Distributing
  • 5.11. Security
  • 5.12. Smoke Tests
  • 5.13. Static Code Analysis
  • 5.14. Test Automation
  • 5.15. Type Checking
  • 5.16. UI testing
  • 5.17. Mutation Testing

Network and HTTP

Network and HTTP

• 1. HTTP and Web
  • 1.1. HTTP Protocol
  • 1.2. HTTP using stdlib
  • 1.3. HTTP using requests
  • 1.4. HTML Scrapping
  • 1.5. HTTP Frameworks
  • 1.6. FastAPI
  • 1.7. Templates with Jinja2
  • 1.8. PaaS - Platform as a Service
  • 1.9. SOAP and WSDL
  • 1.10. Asynchronous processing
• 2. Protocols
  • 2.1. FTP
  • 2.2. IMAP
  • 2.3. SMTP
  • 2.4. POP3
  • 2.5. LDAP
  • 2.6. SSH
  • 2.7. DNS
• 3. Transport
  • 3.1. OSI Model
  • 3.2. Interfaces
  • 3.3. Routing
  • 3.4. Socket
  • 3.5. SSL
  • 3.6. IPv4/IPv6
  • 3.7. TCP/UDP
  • 3.8. scapy
  • 3.9. 3rd Party Libraries
  • 3.10. Mini Botnet

Django

Django

• 1. HTTP
  • 1.1. Narzędzia
  • 1.2. DNS
  • 1.3. HTTP Protocol
  • 1.4. HTTP Methods
  • 1.5. HTML + JS + CSS
• 2. Introduction to Django
  • 2.1. Co to jest Django?
  • 2.2. Dokumentacja
  • 2.3. Wersjonowanie
  • 2.4. setup.cfg
  • 2.5. IDE Support
• 3. Django Framework Architecture
  • 3.1. Framework Architecture
  • 3.2. Request-Response flow
• 4. Installation and Running
  • 4.1. Instalacja
  • 4.2. Starting new project
  • 4.3. IDE Run configuration
• 5. Settings
• 6. Apps
  • 6.1. Reusability
  • 6.2. Location
  • 6.3. Create
  • 6.4. Structure
  • 6.5. Configuration
• 7. Models
  • 7.1. Model Fields
  • 7.2. Model field arguments
  • 7.3. Abstract Models
  • 7.4. Architecture
  • 7.5. Single File vs. Models per file
  • 7.6. Reverse engineering database
  • 7.7. Database schema migration
  • 7.8. Example Model
  • 7.9. Assignments
• 8. URL Router
  • 8.1. Global URLs
  • 8.2. In app URLs
  • 8.3. urlpatterns
  • 8.4. path
  • 8.5. url
  • 8.6. include
  • 8.7. URLs reversing
  • 8.8. URLs in templates
• 9. ORM
  • 9.1. QuerySet
  • 9.2. Field lookups
  • 9.3. Filters can reference fields on the model
  • 9.4. Complex lookups with Q objects
  • 9.5. Comparing objects
  • 9.6. Q() expressions
  • 9.7. F() expressions
  • 9.8. Aggregations
• 10. Views
  • 10.1. Class Based Views
  • 10.2. Generic Views
  • 10.3. Responses
  • 10.4. Permissions in Views
• 11. Forms
  • 11.1. Forms
  • 11.2. Model Forms
  • 11.3. Widgets
• 12. REST
  • 12.1. RESTful Views
  • 12.2. python -m json.tool
  • 12.3. django-rest-framework
  • 12.4. django-rest-swagger
• 13. Staticfiles
  • 13.1. Settings
  • 13.2. Serving staticfiles
• 14. Templates
• 15. Templatetags
  • 15.1. Filters
  • 15.2. Simple Tags
  • 15.3. Assignment tags
  • 15.4. Block tags
• 16. Admin panel
  • 16.1. Customizing
  • 16.2. Permissions
  • 16.3. Model Admin
  • 16.4. Model Inlines
  • 16.5. Extending Admin
  • 16.6. ModelAdmin Example
  • 16.7. django-import-export
  • 16.8. Grapelli
• 17. Management Commands
  • 17.1. Built-in
  • 17.2. Writing own management commands
• 18. i18n and l10n
  • 18.1. Timezone
  • 18.2. Gettext
  • 18.3. i18n - internationalization
• 19. Database
  • 19.1. Database schema migration
  • 19.2. Management commands
  • 19.3. Database settings
  • 19.4. Fixtures
  • 19.5. Multiple DB and db routing
• 20. Cache
• 21. Signals
  • 21.1. Available signals
  • 21.2. Examples
• 22. Auth and Permissions
  • 22.1. Basic Auth
  • 22.2. Require Permissions
• 23. API CORS
• 24. Standalone scripts
• 25. Tests and quality
  • 25.1. Tests
  • 25.2. Test URLs
  • 25.3. SonarQube
  • 25.4. Debug Toolbar
• 26. Tests
• 27. CI/CD - Continuous Integration and Deployment
  • 27.1. Bitbucket Pipelines
  • 27.2. Travis
• 28. Deployment
  • 28.1. Deployment platforms
  • 28.2. Packaging
  • 28.3. Staticfiles serving

Data Science

Data Science

• 1. Introduction
  • 1.1. Description
  • 1.2. Program
  • 1.3. Agenda
  • 1.4. Survey
• 2. Jupyter
  • 2.1. Jupyter
• 3. Python
  • 3.1. Tools and libs
  • 3.2. Python Syntax
  • 3.3. Types
  • 3.4. Sequences
  • 3.5. Mappings
  • 3.6. Conditionals
  • 3.7. Looping
  • 3.8. Control Flow
  • 3.9. Functions
  • 3.10. Object Oriented Programming
  • 3.11. Packages
  • 3.12. Math
  • 3.13. Serialization
  • 3.14. Performance
• 4. Data Visualization
  • 4.1. Plotly
  • 4.2. Bokeh
  • 4.3. Seaborn
  • 4.4. Generowanie grafów
• 5. Scipy
  • 5.1. SciPy

Machine Learning

Machine Learning

• 1. About
  • 1.1. What is Data?
  • 1.2. Basic Statistics
  • 1.3. Machine Learning Introduction
  • 1.4. Glossary PL
  • 1.5. Glossary
• 2. Introduction
  • 2.1. Datasets
  • 2.2. Machine Learning Algorithms
  • 2.3. Features
  • 2.4. Classifiers
• 3. Sklearn
  • 3.1. scikit-learn
• 4. Model Quality
  • 4.1. Model Quality
  • 4.2. Principal Component Analysis
• 5. Decision Trees
  • 5.1. Decision Tree
• 6. Regressions
  • 6.1. Linear Regression
  • 6.2. Logistic Regression
• 7. K-Nearest Neighbors
  • 7.1. K Nearest Neighbors
• 8. Bayes
  • 8.1. Naive Bayes
• 9. Support Vector Machines
  • 9.1. Support Vector Machines
• 10. Clustering
  • 10.1. K-Means Clustering
• 11. Neural Networks
  • 11.1. Deep Neural Network
  • 11.2. Convolutional Neural Network
• 12. References
  • 12.1. References
  • 12.2. Notes

Blogposts

Blogposts

• 1. Wprowadzenie do Machine Learning w Pythonie

Appendixes

Appendixes

• 1. History
• 10.2. Further reading
• 2. Books
• 3. Video
• 4. Python 2 vs. 3
• 5. Python Intermediate
• 6. Python in Networking
• 7. Django Framework
• 8. Python in Testing
• 9. Python in Science and Engineering
• 10. Python in Machine Learning
• 11. References