Python 3: from None to Machine Learning

Author

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

Workshop

Warning

Przed szkoleniem proszę o instalację i konfigurację środowiska zgodnie z 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

Tip

If you’re interested in training course on topics from this book, please email me at matt@astrotech.io

Other books from this series

Python and Machine Learning:  http://python.astrotech.io DevOps and CI/CD wtih Docker:  http://devops.astrotech.io GIT and GIT Flow in CI/CD:  http://git.astrotech.io Agile, Scrum, Kanban, XP, Lean:  http://agile.astrotech.io Software Architecture, Cloud, Microservices and Processes:  http://arch.astrotech.io Linux and Shell Programming with Bash:  http://linux.astrotech.io

Tip

The most up-to-date version of this book is always at http://python.astrotech.io

Introduction

Introduction

• 1. Installing Python
  • 1.1. System Requirements
  • 1.2. Setting-up Environment
• 2. References in the book
  • 2.1. Iris Dataset
  • 2.2. Ehlo World!
  • 2.3. Addresses
  • 2.4. Dates and timezones
  • 2.5. Main Characters
• 3. About Python Language
  • 3.1. What is Python?
  • 3.2. Which version?
  • 3.3. Scripts
  • 3.4. Python Console (REPL)
  • 3.5. Jupyter
• 4. IDE - Integrated Development Environment
  • 4.1. What is IDE?
  • 4.2. How to choose?
  • 4.3. Which one is the best?
  • 4.4. PyCharm
  • 4.5. Assignments
• 5. Syntax
  • 5.1. Indentation instead of braces
  • 5.2. End of lines
  • 5.3. Comments
  • 5.4. Variables and constants
  • 5.5. print()
  • 5.6. Assignments

Data Types

Data Types

• 1. int
  • 1.1. Defining int
  • 1.2. Converting to int
  • 1.3. Numerical Operators
• 2. float
  • 2.1. Defining float
  • 2.2. Converting to float
  • 2.3. Numeric Functions
  • 2.4. Assignments
• 3. bool
  • 3.1. Defining bool
  • 3.2. Converting to bool
  • 3.3. Negative values
  • 3.4. Boolean logic
  • 3.5. Logic operators
  • 3.6. Assignments
• 4. None
  • 4.1. Defining None
  • 4.2. Assignments
• 5. str
  • 5.1. Defining str
  • 5.2. Type casting to str
  • 5.3. Single or double quote?
  • 5.4. Escape characters
  • 5.5. Characters before strings
  • 5.6. String methods
  • 5.7. Getting text from user
  • 5.8. Cleaning str from user input
  • 5.9. Assignments
• 6. list
  • 6.1. Defining list
  • 6.2. Adding elements
  • 6.3. Slicing list
  • 6.4. sorted() vs. list.sort()
  • 6.5. Built-in functions on sequences
  • 6.6. Membership Operators
  • 6.7. Length of a list
• 7. set
  • 7.1. Defining set
  • 7.2. Adding items
  • 7.3. set arithmetics
  • 7.4. Slicing set
  • 7.5. Length of a set
  • 7.6. Converting list to set deduplicate items
• 8. tuple
  • 8.1. Defining tuple
  • 8.2. Slicing tuple
  • 8.3. Length of a tuple
• 9. dict
  • 9.1. Defining dict
  • 9.2. Adding elements
  • 9.3. Accessing elements
  • 9.4. Length of a dict
  • 9.5. Assignments
• 10. Nested Collections
  • 10.1. list of tuple
  • 10.2. list of dict
  • 10.3. list of list
  • 10.4. Mixed types
  • 10.5. Assignments
• 11. Type System
  • 11.1. Type inference
  • 11.2. Type Hinting A.K.A. Type Annotation
  • 11.3. Problematic types

Control Flow

Control Flow

• 1. Slicing
  • 1.1. Accessing element with index
  • 1.2. Accessing range of elements
  • 1.3. Slice function
  • 1.4. Reversing and stepping over elements
  • 1.5. Assignments
• 2. Unpacking sequences
  • 2.1. Unpacking values
  • 2.2. Unpacking arbitrary number of arguments
  • 2.3. More advanced topics
  • 2.4. Assignments
• 3. Conditional Statements
  • 3.1. if
  • 3.2. else
  • 3.3. Inline if
  • 3.4. elif and switch
  • 3.5. Assignments
• 4. Composite statements
  • 4.1. Complex expressions
  • 4.2. Control Statements
  • 4.3. Assignments
• 5. Operators
  • 5.1. Operator precedence
  • 5.2. Assignments
• 6. While loop
  • 6.1. Declaration
  • 6.2. break and continue
  • 6.3. Assignments
• 7. for loop
  • 7.1. Syntax
  • 7.2. Iterating over str
  • 7.3. Iterating simple collections
  • 7.4. Assignments
• 8. Loops and nested objects
  • 8.1. Iterating over nested list items
  • 8.2. Unpacking values in loop
  • 8.3. Enumerating and item index
  • 8.4. Iterating over dict items
  • 8.5. Iterating complex types
  • 8.6. Assignments
• 9. Comprehensions
  • 9.1. Simple usage
  • 9.2. Conditional Comprehension
  • 9.3. Why?
  • 9.4. Advanced usage for Comprehensions and Generators
  • 9.5. Assignments
• 10. Exceptions
  • 10.1. What are and why to use exceptions?
  • 10.2. Most common exceptions
  • 10.3. Raising exceptions
  • 10.4. Tracebacks
  • 10.5. Catching exceptions
  • 10.6. Exception hierarchy
  • 10.7. Defining own exceptions
  • 10.8. Real life use-case
• 11. Files
  • 11.1. Path
  • 11.2. Access modes
  • 11.3. Read from file
  • 11.4. Writing to file
  • 11.5. Appending to file
  • 11.6. Exception handling
  • 11.7. Good Engineering Practises
  • 11.8. Assignments
• 12. Exam from basic topics
  • 12.1. Parsowanie /etc/passwd

Functions

Functions

• 1. Function Basics
  • 1.1. Function definition
  • 1.2. Returning values
  • 1.3. Function arguments
  • 1.4. Naming convention
  • 1.5. Variable scope
  • 1.6. More advanced topics
  • 1.7. Assignments
• 2. Unpacking return elements
  • 2.1. * and ** operators
  • 2.2. Unpacking values from function
  • 2.3. Example
  • 2.4. Assignments
• 3. Functions with many arguments
  • 3.1. Operands * and **
  • 3.2. Recap information about function parameters
  • 3.3. Defining function with many arguments
  • 3.4. Case Study
  • 3.5. Assignment
• 4. Passing many arguments
  • 4.1. Operators * i **
  • 4.2. * unpacks list or tuple
  • 4.3. ** unpacks dict
  • 4.4. Przekazywanie do funkcji zmiennej ilości parametrów
  • 4.5. Przykładowe zastosowanie
  • 4.6. Assignments
• 5. Recurrence
  • 5.1. What is recurrence?
• 6. Advanced Functions
  • 6.1. Type annotations
  • 6.2. Callable
  • 6.3. Assignments

Standard Library

Standard Library

• 1. Installing Packages
  • 1.1. Virtualenv
  • 1.2. Searching
  • 1.3. Installing
  • 1.4. Using
  • 1.5. Importing
  • 1.6. Assignments
• 2. Builtin and Keywords
  • 2.1. Keywords
  • 2.2. Builtin functions
  • 2.3. Other builtin functions
  • 2.4. Assignments
• 3. Mathematics
  • 3.1. complex
  • 3.2. Builtin
  • 3.3. math
  • 3.4. fractions
  • 3.5. statistics
  • 3.6. random
  • 3.7. collections.Counter
  • 3.8. matplotlib
  • 3.9. Assignments
• 4. Numerical Precision
  • 4.1. Problem
  • 4.2. float anatomy
  • 4.3. decimal
• 5. Print Formatting
  • 5.1. Funkcja print
  • 5.2. Konkatenacja stringów
  • 5.3. Interpolacja zmiennych
  • 5.4. Metoda .format()
  • 5.5. f-strings - Python >= 3.6
  • 5.6. SQL Injection
  • 5.7. PEP 3101 – Advanced String Formatting
  • 5.8. Więcej informacji
  • 5.9. pprint
  • 5.10. Assignments
• 6. Regular Expressions
  • 6.1. Constructing Regular Expressions
  • 6.2. Most frequent used functions in re module
  • 6.3. RegEx parameters (variables)
  • 6.4. Multi line searches
  • 6.5. Greedy and non-greedy search
  • 6.6. Practical example of Regex usage
  • 6.7. Assignments

Date and Time

Date and Time

• 1. Dates and Time
  • 1.1. Creating date objects
  • 1.2. Creating time objects
  • 1.3. Creating datetime objects
  • 1.4. datetime methods
  • 1.5. Assignments
• 2. Parsing and Formatting
  • 2.1. Date and time formats
  • 2.2. ISO 8601 Standard
  • 2.3. Table of date and time parsing and formatting parameters
  • 2.4. f-string formatting
  • 2.5. Format to string
  • 2.6. Parsing dates
  • 2.7. Assignments
• 3. Time Deltas
  • 3.1. Time shifts
  • 3.2. timedelta
  • 3.3. Time diff
  • 3.4. Assignments
• 4. Timezones
  • 4.1. Time zones in datetime library
  • 4.2. pytz
• 5. Timestamp
  • 5.1. datetime
  • 5.2. time
• 6. Time Utils
  • 6.1. Sleep
  • 6.2. calendar
  • 6.3. timeit - Microbenchmarks

Serialization

Serialization

• 1. CSV Serialization
  • 1.1. Reading data from CSV files
  • 1.2. Writing to CSV files
  • 1.3. Parsing non-CSV files with csv.DictReader()
  • 1.4. Assignments
• 2. JSON Serialization
  • 2.1. JSON syntax
  • 2.2. JSON Serialization of simple objects
  • 2.3. Serializing datetime and date
  • 2.4. Class based encoders and decoders
  • 2.5. Serializing objects
  • 2.6. Pretty Printing JSON
  • 2.7. Assignments
• 3. XML
  • 3.1. xml
  • 3.2. lxml
  • 3.3. xslt
  • 3.4. Assignments
• 4. Pickle serialization
  • 4.1. Serializing objects
  • 4.2. Deserializing objects
  • 4.3. Assignments
• 5. Databases
  • 5.1. SQL Syntax
  • 5.2. DB API
  • 5.3. sqlite3
  • 5.4. pyMySQL
  • 5.5. psycopg2
  • 5.6. pymongo
  • 5.7. SQL Injection
  • 5.8. ORM
  • 5.9. Database Schema Migration
  • 5.10. Data exploration
  • 5.11. Case Study
  • 5.12. Assignments
• 6. SQL
  • 6.1. ACID
  • 6.2. Data Types
  • 6.3. Constrains
  • 6.4. CREATE
  • 6.5. INSERT
  • 6.6. AUTOINCREMENT
  • 6.7. COMMIT and ROLLBACK
  • 6.8. SELECT
  • 6.9. UPDATE
  • 6.10. GROUP BY
  • 6.11. HAVING
  • 6.12. ALTER
  • 6.13. DROP
  • 6.14. DELETE
  • 6.15. JOIN
  • 6.16. TRUNCATE
  • 6.17. SQL Injection

Object Oriented Programming

Object Oriented Programming

• 1. OOP Basic
  • 1.1. Object Paradigm
  • 1.2. Classes
  • 1.3. Classes vs Objects
  • 1.4. Fields
  • 1.5. Methods
  • 1.6. Initializer Method
  • 1.7. Stringify object
  • 1.8. One class per file?
  • 1.9. Assignments
• 2. OOP Intermediate
  • 2.1. Fields
  • 2.2. Initial arguments mutability and shared state
  • 2.3. Inheritance
  • 2.4. More advanced topics
  • 2.5. Assignments
• 3. Dataclass
  • 3.1. Example 1
  • 3.2. Field Factory
  • 3.3. __init__ vs. __post_init__
  • 3.4. Case Study
  • 3.5. More advanced options
  • 3.6. Under the hood
  • 3.7. Assignments
• 4. Enum
  • 4.1. Why?
  • 4.2. Definition
  • 4.3. Usage
  • 4.4. Case Study
  • 4.5. Assignments
• 5. Operator Overload
  • 5.1. Why to use operator overload?
  • 5.2. Numerical Operator Overload
  • 5.3. Logical Operator Overload
  • 5.4. Boolean Operator Overload
  • 5.5. Builtins Function Overload
  • 5.6. Accessors Overload
  • 5.7. Assignment
• 6. OOP Paradigm
  • 6.1. Everything is an object
  • 6.2. Duck typing
  • 6.3. Private, protected, public?!
  • 6.4. Inheritance vs. Composition (Mixin Classes)
  • 6.5. Polymorphism
  • 6.6. Interfaces
  • 6.7. Abstract Classes
  • 6.8. Good Engineering Practises
  • 6.9. S.O.L.I.D.
  • 6.10. GRASP
  • 6.11. Dependency injection
• 7. OOP Advanced
  • 7.1. __str__() and __repr__()
  • 7.2. What should be in the class and what not?
  • 7.3. Dynamically creating fields
  • 7.4. Setter and Getter
  • 7.5. Hash
  • 7.6. is
  • 7.7. Monkey Patching
  • 7.8. Method Resolution Order
  • 7.9. Objects and instances
  • 7.10. Metaclass
  • 7.11. Assignments
• 8. Metaclass

Good Engineering Practice

Good Engineering Practice

• 1. Software Engineering Conventions
  • 1.1. PEP 8 - Style Guide for Python Code
  • 1.2. PEP 20 - The Zen of Python
  • 1.3. Magic number i magic string
  • 1.4. pycodestyle
  • 1.5. Assignment
• 2. Logging
  • 2.1. Korzystanie z logging
  • 2.2. Konfiguracja formatowania logów
  • 2.3. Further Reading
• 3. Warnings
  • 3.1. Usage
  • 3.2. Running
  • 3.3. Example
• 4. Basic Debugging
  • 4.1. Run in the console
  • 4.2. print
  • 4.3. pprint
  • 4.4. locals()
  • 4.5. Using debugger in IDE
• 5. Doctest
  • 5.1. Test for bool return values
  • 5.2. Test for int return values
  • 5.3. Test for str return values
  • 5.4. Testing for exceptions
  • 5.5. Using python statements in doctest
  • 5.6. Running doctest from standalone scripts
  • 5.7. Practical example
  • 5.8. Assignments
• 6. Unittesting
  • 6.1. Glossary
  • 6.2. Using unittest
  • 6.3. Assignments
• 7. Introspection
  • 7.1. Introspecting Types
  • 7.2. Introspecting Objects
  • 7.3. Introspecting Docstrings
  • 7.4. Example

Design Patterns

Design Patterns

• 1. Generators and Comprehensions
  • 1.1. Lazy evaluation
  • 1.2. Generator expressions vs. Comprehensions
  • 1.3. Operator yield
  • 1.4. The whys and wherefores
  • 1.5. Assignments
• 2. Iterators
  • 2.1. What is iterator?
  • 2.2. Iterowanie po obiektach
  • 2.3. Własny iterator
  • 2.4. itertools
  • 2.5. Przykład
  • 2.6. Assignments
• 3. Context Managers
  • 3.1. About Context Managers
  • 3.2. Contextmanager decorator
  • 3.3. Assignments
• 4. Descriptor
  • 4.1. The Descriptor Protocol
  • 4.2. Builtin Descriptor Object Examples
  • 4.3. Example
  • 4.4. Accessors
  • 4.5. Assignments
• 5. Decorator
  • 5.1. Example
  • 5.2. Zastosowanie
  • 5.3. Function Decorators
  • 5.4. Class Decorators
  • 5.5. functools
  • 5.6. Przykład
  • 5.7. Assignments
• 6. Gang of Four
  • 6.1. Examples
  • 6.2. Structural Design Patterns
  • 6.3. Creational Design Patterns
  • 6.4. Behavioral Design Patterns
  • 6.5. Idiomy języka programowania

HTTP and Web

HTTP and Web

• 1. HTTP Protocol
  • 1.1. HTTP Methods
  • 1.2. HTTP Statuses
  • 1.3. HTTP Headers
  • 1.4. MIME types
  • 1.5. API Versioning
• 2. HTTP using stdlib
  • 2.1. http.HTTPStatus
  • 2.2. urllib
  • 2.3. http.server
  • 2.4. http.client
  • 2.5. Assignments
• 3. HTTP using requests
  • 3.1. Basic API
  • 3.2. Response
  • 3.3. GET Requests
  • 3.4. POST Requests
  • 3.5. DELETE Requests
  • 3.6. Custom Headers
  • 3.7. Timeout
  • 3.8. Basic Auth
  • 3.9. Requests OAuth
  • 3.10. Assignments
• 4. HTML Scrapping
  • 4.1. BeautifulSoup
  • 4.2. Assignments
• 5. HTTP Frameworks
  • 5.1. atlassian-python-api
  • 5.2. Standard WSGI
  • 5.3. django
  • 5.4. flask
  • 5.5. webapp2
  • 5.6. tornado
• 6. Templates with Jinja2
  • 6.1. Why?
  • 6.2. Syntax
  • 6.3. Example usage
  • 6.4. Method Calls
  • 6.5. Filters
  • 6.6. Assignment tag
  • 6.7. Include
  • 6.8. Conditionals
  • 6.9. Loops
  • 6.10. Blocks
  • 6.11. Cycle
  • 6.12. Base Template
  • 6.13. Import Macros
  • 6.14. i18n Trans
• 7. PaaS - Platform as a Service
  • 7.1. Heroku
  • 7.2. Amazon AWS
  • 7.3. Google App Engine
• 8. SOAP and WSDL
  • 8.1. WSDL
  • 8.2. suds

Network

Network and HTTP

• 1. FTP
  • 1.1. Connect to FTP Server (insecure)
  • 1.2. Connect to FTP Server (secure)
  • 1.3. Download file
  • 1.4. Methods
• 2. IMAP
  • 2.1. Connection
  • 2.2. Methods
  • 2.3. Usage
  • 2.4. Case Study for Gmail IMAP
• 3. SMTP
  • 3.1. Send plaintext email
  • 3.2. Send email with attachments
  • 3.3. Secured connection to the SMTP server
• 4. POP3
  • 4.1. Connection
  • 4.2. poplib API
  • 4.3. Retrieve Messages with POP3
• 5. LDAP
  • 5.1. ldap3
• 6. DNS Protocol
  • 6.1. dnspython
• 7. SSH Protocol
  • 7.1. paramiko
  • 7.2. fabric
  • 7.3. pssh
• 8. Interfaces
  • 8.1. netifaces
• 9. IPv4/IPv6
  • 9.1. ipaddress (stdlib)
  • 9.2. netaddr (3rd party)
• 10. TCP/UDP
  • 10.1. TCP
  • 10.2. scapy
• 11. Socket
  • 11.1. socket
  • 11.2. socketserver
  • 11.3. References
• 12. SSL
  • 12.1. Socket creation
  • 12.2. SSLSocket
  • 12.3. Client-side operation
  • 12.4. Server-side operation
• 13. 3rd Party Libraries
  • 13.1. Allegro Ralph
  • 13.2. Pcapy and libpcap
  • 13.3. Dpkt
  • 13.4. Scanpy
  • 13.5. Twisted
  • 13.6. Pyro4
  • 13.7. Celery
  • 13.8. RabbitMQ
• 14. Mini Botnet
  • 14.1. Assignment

CI/CD, Versioning and Distributing

CI/CD, Versioning and Distributing

• 1. Tools for CI/CD
  • 1.1. Frontend Testing
  • 1.2. Static Code Analysis
  • 1.3. Behavioral Testing
  • 1.4. Type Checking
  • 1.5. Automation and Releases
  • 1.6. Testy Mutacyjne
  • 1.7. Translation
• 2. Type Annotation
  • 2.1. Sprawdzanie typów
  • 2.2. Dodawanie typów do instniejącego kodu
  • 2.3. Korzystanie z typów
  • 2.4. Introspekcja
• 3. Virtualenv
  • 3.1. Tworzenie wirtualnego środowiska
  • 3.2. Aktywacja i korzystanie ze środowiska
  • 3.3. Dobra praktyka
  • 3.4. Pakiety i zależności
• 4. Standalone Applications
  • 4.1. zipapp
• 5. Distributing Packages
  • 5.1. Instalacja i korzystanie z zewnętrznych bibliotek
  • 5.2. Importowanie modułów
  • 5.3. Modularyzacja
  • 5.4. Tworzenie paczek
  • 5.5. Przyszłość paczkowania i dystrybucji
• 6. Commandline argument parsing
  • 6.1. argparse
  • 6.2. Example
  • 6.3. docopt
  • 6.4. Assignments
• 7. Operating System
  • 7.1. Bitwise operators
  • 7.2. Accessing Environmental Variables
  • 7.3. Getting filenames and extensions
  • 7.4. Checking OS version
  • 7.5. sys
  • 7.6. os
  • 7.7. subprocess
  • 7.8. tempfile
  • 7.9. io
  • 7.10. configparser
  • 7.11. pathlib
  • 7.12. Running commands in parallel across many hosts
  • 7.13. Passwords and secrets
  • 7.14. Allegro Tipboard
  • 7.15. Assignments
• 8. ReST and Sphinx documentation
  • 8.1. reStructuredText vs. Markdown
  • 8.2. Format reStructuredText
  • 8.3. Sphinx
  • 8.4. Read the docs
  • 8.5. Assignments

High Performance

High Performance

• 1. Performance Optimization
  • 1.1. PyPy
  • 1.2. Seven strategies
  • 1.3. Use set instead of list
  • 1.4. Use list.append() instead of str + str
  • 1.5. Range between two float
  • 1.6. Inne
  • 1.7. Further Reading
  • 1.8. Assignments
• 2. Compilers and interpreters
  • 2.1. Interpreters
  • 2.2. Compiling
• 3. Collections
  • 3.1. OrderedDict
  • 3.2. namedtuple
• 4. Queues, Threads and Processes
  • 4.1. Kolejki - Queue
  • 4.2. Threads vs processes
  • 4.3. Threads
  • 4.4. Processes
  • 4.5. Assignments
• 5. Concurrency
  • 5.1. High-Level Concurrency
  • 5.2. Coroutine
  • 5.3. Asyncio
  • 5.4. Przykłady praktyczne
  • 5.5. Trio
• 6. C Extensions
  • 6.1. C Types
  • 6.2. C Modules
  • 6.3. Assignments
• 7. Functional Programming
  • 7.1. Rekurencja
  • 7.2. Lambda - funkcje anonimowe
  • 7.3. Closure
  • 7.4. Monady
  • 7.5. złożenia funkcji
  • 7.6. functools
  • 7.7. Callback
  • 7.8. Assignments
• 8. Advanced Debugging
  • 8.1. json.tool
  • 8.2. Using pdb
  • 8.3. breakpoint()
  • 8.4. Using debugger in IDE
  • 8.5. Debugging i Wątki
  • 8.6. Debugging i Procesy
  • 8.7. Debugging aplikacji sieciowych
  • 8.8. Assignment

Scientific and Engineering

Scientific and Engineering

• 1. Biblioteki w zastosowaniach naukowych i inżynieryjnych
  • 1.1. Środowisko
  • 1.2. Ecosystem
  • 1.3. Biblioteki do obsługi danych
  • 1.4. Wykresy i grafy
  • 1.5. Biblioteki do nauczania maszynowego
  • 1.6. Other
• 2. Zastosowanie naukowe
  • 2.1. Scipy

Numerical Analysis

Numerical Analysis

• 1. Jupyter
  • 1.1. Install
  • 1.2. Run
  • 1.3. Using
  • 1.4. Shortcut keys
  • 1.5. Cells
  • 1.6. Run
  • 1.7. Magic commands
  • 1.8. Kernels
  • 1.9. Functions
  • 1.10. Performance and profiling
  • 1.11. Markdown
  • 1.12. Embedding objects
  • 1.13. Workflow
  • 1.14. Execute terminal commands
  • 1.15. Output to different formats
  • 1.16. Assignments
• 2. Numpy
  • 2.1. Import
  • 2.2. Functions
  • 2.3. Random numbers
  • 2.4. Polynomials
  • 2.5. Arrays
  • 2.6. Statistics
  • 2.7. Vector and matrix mathematics
  • 2.8. Matrix
  • 2.9. Linear Algebra
  • 2.10. Assignments
• 3. Numpy
  • 3.1. Import
  • 3.2. Data Structures
  • 3.3. Functions
  • 3.4. Statistics
  • 3.5. Random numbers
  • 3.6. Polynomial mathematics
  • 3.7. Arrays
  • 3.8. Vector and matrix mathematics
  • 3.9. Matrix
  • 3.10. Linear Algebra
  • 3.11. Assignments
• 4. Pandas Introduction
  • 4.1. TL;DR
  • 4.2. Import and Export
  • 4.3. Display Output
  • 4.4. Vizualization
  • 4.5. Descriptive statistics
  • 4.6. Assignments
• 5. Pandas Series
  • 5.1. Creating
  • 5.2. Indexes
  • 5.3. Slicing
  • 5.4. Arithmetic operations
  • 5.5. Assignments
• 6. Pandas DataFrame
  • 6.1. Creating
  • 6.2. Properties
  • 6.3. Slicing
  • 6.4. Filtering
  • 6.5. Locating values
  • 6.6. Accessing values
  • 6.7. Modifying values
  • 6.8. Selecting values
  • 6.9. Sorting
  • 6.10. Statistics
  • 6.11. Grouping
  • 6.12. Aggregations
  • 6.13. Joins
  • 6.14. Practical Example
  • 6.15. Assignments

Data Vizualization

Data Vizualization

• 1. Matplotlib
  • 1.1. Glossary
  • 1.2. What is matplotlib
  • 1.3. Installing and using matplotlib
  • 1.4. How to understand charts?
  • 1.5. Simple examples
  • 1.6. Labels and Legend
  • 1.7. Basic customizations
  • 1.8. Additional info
  • 1.9. Working with multiple figures and axes
  • 1.10. Working with text
  • 1.11. Logarithmic and other nonlinear axes
  • 1.12. plt.plot() vs ax.plot()
  • 1.13. Assignment
• 2. Matplotlib Pandas
  • 2.1. Visualizing data
• 3. Matplotlib Charts
  • 3.1. Bars
  • 3.2. Histogram
  • 3.3. Scatter plot
  • 3.4. Stack plot
  • 3.5. Pie chart
  • 3.6. Nested Pie Chart
  • 3.7. 3D
  • 3.8. Assignments
• 4. Matplotlib Plot Lifecycle
  • 4.1. A note on the Object-Oriented API vs Pyplot
  • 4.2. Our data
  • 4.3. Getting started
  • 4.4. Controlling the style
  • 4.5. Customizing the plot
  • 4.6. Combining multiple visualizations
  • 4.7. Saving our plot
  • 4.8. Assignment
• 5. Matplotlib Examples
  • 5.1. Gallery
  • 5.2. Matplotlib and Jupyter
  • 5.3. Points
  • 5.4. Line Chart
  • 5.5. Multiple Charts
  • 5.6. Box Chart
  • 5.7. Bars
  • 5.8. Annotate
  • 5.9. Scales
  • 5.10. Pie Chart
  • 5.11. 3D
  • 5.12. Grid
  • 5.13. Legend using pre-defined labels
  • 5.14. Radar Chart
• 6. Plotly
  • 6.1. Installation
  • 6.2. Offline usage
  • 6.3. Jupyter Usage
  • 6.4. Plot Controls & IPython widgets
  • 6.5. Charts
  • 6.6. Time Series
  • 6.7. Two plots
  • 6.8. 3D Plotting
• 7. Bokeh
  • 7.1. Installation
  • 7.2. Download Sample Data
  • 7.3. Charts
• 8. Seaborn
• 9. Generowanie grafów
  • 9.1. PyDotPlus
  • 9.2. Graphviz
• 10. Projektowanie GUI
  • 10.1. Graphical User Interfaces with Tkinter
  • 10.2. Biblioteka Tkinter

Machine Learning

Machine Learning

• 1. Wprowadzenie do statystyki
  • 1.1. Rozkłady
  • 1.2. Podstawowe pojęcia
• 2. Wprowadzenie do Machine Learning
  • 2.1. Czym jest uczenie maszynowe?
  • 2.2. Co trzeba wiedzieć przed rozpoczęciem pracy?
  • 2.3. Ważne pytania przed przystąpieniem do tworzenia algorytmu
  • 2.4. Czyszczenie Danych
  • 2.5. Praca z bibliotekami
  • 2.6. Kategorie algorytmów uczenia maszynowego
• 3. Słowniczek intuicyjny
• 4. Słownik Pojęć
• 5. Datasets
  • 5.1. Skąd wziąć dane testowe?
• 6. Machine Learning Algorithms
  • 6.1. Regression Algorithms
  • 6.2. Instance-based Algorithms
  • 6.3. Regularization Algorithms
  • 6.4. Decision Tree Algorithms
  • 6.5. Bayesian Algorithms
  • 6.6. Clustering Algorithms
  • 6.7. Assoication Rule Learning Algorithms
  • 6.8. Artificial Neural Network Algorithms
  • 6.9. Deep Learning Algorithms
  • 6.10. Dimensional Reduction Algorithms
  • 6.11. Ensemble Algorithms
  • 6.12. Other Algorithms
• 7. Features
  • 7.1. Czym są features?
  • 7.2. Przykład praktyczny
  • 7.3. What Makes a Good Feature?
  • 7.4. Assignments
• 8. Classifiers
  • 8.1. Co to jest Classifier?
  • 8.2. Schemat działania classifiera
• 9. scikit-learn
  • 9.1. Loading Sample Datasets
  • 9.2. Fit and Predict
  • 9.3. Classifier
  • 9.4. Feature Selection
  • 9.5. Evaluation
  • 9.6. Label Encoder
  • 9.7. Writing Own Classifier
  • 9.8. Zadania praktyczne
• 10. Model Quality
  • 10.1. Metody doboru modelu i poprawienia jakości
  • 10.2. Słownictwo
  • 10.3. Diagnostyka Bias vs. Wariancja
  • 10.4. Model Evaluation Procedure
  • 10.5. Regularyzacja
  • 10.6. Random Forrest
  • 10.7. Ensemble averaging
• 11. Decision Tree
  • 11.1. Visualizing a Decision Tree
  • 11.2. Zadania praktyczne
• 12. k Nearest Neighbors
  • 12.1. Jak działa algorytm “K najbliższych sąsiadów”
  • 12.2. Przykład praktyczny
  • 12.3. Określanie przynależności do zbioru
  • 12.4. Wyznaczanie odległości
  • 12.5. Zalety i wady
  • 12.6. Assignments
• 13. Bayes
  • 13.1. Co to jest Naiwny klasyfikator Bayesowski?
  • 13.2. Gaussian Naive Bayes
  • 13.3. Przykłady praktyczne
  • 13.4. Plusy i minusy Naiwnego Bayesa
  • 13.5. Aplikacje Naiwnego klasyfikatora Bayesa
  • 13.6. Przykłady praktyczne
  • 13.7. Zadanie kontrolne
• 14. Linear Regression
  • 14.1. Co to jest Linear Regression?
  • 14.2. Przed zastosowaniem
  • 14.3. Wyznaczanie równania prostej
  • 14.4. Funkcja przedziałami liniowa
  • 14.5. Przykłady praktyczne
  • 14.6. Assignments
• 15. Logistic Regression
  • 15.1. Co to jest Logistic Regression?
  • 15.2. Linear vs Logistics
  • 15.3. Podstawowe pojęcia
  • 15.4. Przykład zastosowania
  • 15.5. Assignments
• 16. Support Vector Machines
  • 16.1. TL;DR
  • 16.2. Charakterystyka algorytmu
  • 16.3. Opis algorytmu
  • 16.4. Support Vector Machines (Kernels)
  • 16.5. Przykłady praktyczne
  • 16.6. Assignments
  • 16.7. Lektura uzupełniająca
• 17. Clustering
  • 17.1. Przykłady zastosowania
  • 17.2. Algorytmy klastrowania
  • 17.3. Flat Clustering
  • 17.4. Hierarchical Clustering
  • 17.5. Porównanie algorytmów
  • 17.6. Przykład praktyczny
  • 17.7. Assignments
• 18. Principal Component Analysis (PCA)
  • 18.1. What is Principal Component Analysis?
  • 18.2. 2D example
  • 18.3. 3D example
  • 18.4. Przykłady praktyczne
  • 18.5. Assignments
• 19. Neural Network
  • 19.1. What Is a Neural Network?
  • 19.2. Słownictwo
  • 19.3. Przykład praktyczny
  • 19.4. Tools
  • 19.5. Działanie na sieciach neuronowych
  • 19.6. Przetwarzanie obrazów na przykładzie rozpoznawania odręcznie napisanych cyfr (MNIST)
  • 19.7. Przykłady praktyczne
  • 19.8. Assignments
• 20. Convolutional Neural Network
  • 20.1. Problemy z przetwarzaniem obrazów
  • 20.2. What is and Kernel Convolution?
  • 20.3. What is Convolutional Neural Network (CNN / ConvNet)
  • 20.4. Przydatne odnośniki
• 21. Przydatne odnośniki
  • 21.1. Neural Networks
  • 21.2. Understanding the Bias-Variance Tradeoff
  • 21.3. Bayes
  • 21.4. PyMC3
  • 21.5. Inne
• 22. Notes
  • 22.1. Program
  • 22.2. Program 2
  • 22.3. Notatki
  • 22.4. Data Science
  • 22.5. Jupyter
  • 22.6. Machine Learning
  • 22.7. Metody bez nadzoru

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. Customizong
  • 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. 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. Staticfils serving

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

Workshop

Appendixes

Appendixes

• 1. History
• 2. BDFL - Guido van Rossum
• 3. Name
• 4. PSF - Python Software Foundation
• 5. Further reading
  • 5.1. Documentation
  • 5.2. Working with Excel
  • 5.3. IDE - Integrated Development Environment
  • 5.4. Others
  • 5.5. IT Fun
  • 5.6. Wyzwania i potyczki
• 6. Books
  • 6.1. Algorithms
  • 6.2. Databases
  • 6.3. Software Engineering Practises
  • 6.4. Design patterns
  • 6.5. Refactoring
  • 6.6. Python
• 7. Video
  • 7.1. Collections of videos
  • 7.2. Python
  • 7.3. Machine Learning and Data Science
  • 7.4. Filmy z ostatnich konferencji
• 8. Python 2 vs. 3
  • 8.1. six
  • 8.2. Lista kompatybilności Python 2 i Python 3
  • 8.3. Unicode i kodowanie znaków
  • 8.4. Biblioteki standardowe w 2 i 3
• 9. Python WAT?!
• 10. TODO
  • 10.1. Book TODO
  • 10.2. Python PEP
• 11. Copyright
  • 11.1. MIT License
• 12. Bibliography
• 13. Glossary