Python 3: from None to Machine Learning

Tip

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

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

Table 1. Other books from author

URL	Title
http://python.astrotech.io	Python 3: from None to Machine Learning
http://devops.astrotech.io	DevOps and CI/CD with Docker
http://jira.astrotech.io	Jira Software, Jira Service Desk and Jira Core
http://git.astrotech.io	GIT and GIT Flow
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

Tip

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

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
  • 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

Python basics

Python basics

• 1. Syntax
  • 1.1. Basic Syntax
• 2. Numeric Types
  • 2.1. int
  • 2.2. float
• 3. Logic Types
  • 3.1. bool
  • 3.2. None
• 4. Character Types
  • 4.1. str
  • 4.2. str Methods
• 5. Sequence Types
  • 5.1. list
  • 5.2. tuple
  • 5.3. set
  • 5.4. Nested Collections
  • 5.5. Indexes
  • 5.6. Slices
• 6. Mapping Types
  • 6.1. dict
• 7. Conditionals
  • 7.1. Conditional Statements
  • 7.2. Composite statements
  • 7.3. Operators
• 8. Loops
  • 8.1. while loop
  • 8.2. for loop
  • 8.3. Iterating nested sequences
  • 8.4. Looping over dict
  • 8.5. else in the loop
  • 8.6. Comprehensions
• 9. Control Flow
  • 9.1. Unpacking sequences
  • 9.2. Exceptions
  • 9.3. Files
  • 9.4. Exam
• 10. Functions
  • 10.1. Function Basics
  • 10.2. Types
  • 10.3. Recurrence
  • 10.4. Doctest
• 11. Object Oriented Programming
  • 11.1. Classes and Instances
  • 11.2. Attributes
  • 11.3. Methods
  • 11.4. Initializer Method
  • 11.5. Stringify objects
  • 11.6. Inheritance
  • 11.7. Enum
  • 11.8. Operator Overload
  • 11.9. Dataclass
  • 11.10. Exam

Standard Library

Standard Library

• 1. Builtin functions
  • 1.1. range()
  • 1.2. Sprawdzanie typów
  • 1.3. Działania na kolekcjach
  • 1.4. Konwersje typów
  • 1.5. Other builtin functions
  • 1.6. Assignments
• 2. Keywords
  • 2.1. List of keywords
  • 2.2. pass
  • 2.3. __file__
  • 2.4. del
• 3. Print Formatting
  • 3.1. print function
  • 3.2. String concatenation
  • 3.3. Variable interpolation
  • 3.4. Advanced String Formatting
  • 3.5. pprint
  • 3.6. Assignments
• 4. venv
  • 4.1. venv Module
  • 4.2. Create venv
  • 4.3. Activate Virtualenv
  • 4.4. Good practices
  • 4.5. Assignments
• 5. Installing Packages
  • 5.1. Searching
  • 5.2. Installing
  • 5.3. List requirements
  • 5.4. Assignments
• 6. Importing modules
  • 6.1. Importing modules
  • 6.2. What is Python Module
  • 6.3. Example
  • 6.4. Import path
  • 6.5. __name__

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. Assignments
• 2. Parsing and Formatting
  • 2.1. Date formats
  • 2.2. Time formats
  • 2.3. ISO 8601 Standard
  • 2.4. Date and time parsing and formatting parameters
  • 2.5. Date formatting
  • 2.6. Parsing dates
  • 2.7. Example
  • 2.8. Assignments
• 3. Time Deltas
  • 3.1. Time shifts
  • 3.2. timedelta
  • 3.3. Time diff
  • 3.4. Assignments
• 4. Timestamp
  • 4.1. What is timestamp?
  • 4.2. Get current timestamp
  • 4.3. Convert timestamp to datetime
  • 4.4. Assignments
• 5. Time Utils
  • 5.1. Sleep
  • 5.2. calendar
• 6. Timezones
  • 6.1. Time zones in datetime library
  • 6.2. pytz
  • 6.3. Assignments

Serialization

Serialization

• 1. CSV Serialization
  • 1.1. Dialects
  • 1.2. Read data from CSV file
  • 1.3. Write data to CSV file
  • 1.4. Parsing non-CSV files
  • 1.5. Pandas
  • 1.6. Assignments
• 2. JSON Serialization
  • 2.1. JSON syntax
  • 2.2. JSON Serialization of simple objects
  • 2.3. Serializing datetime and date
  • 2.4. Serializing objects
  • 2.5. Pretty Printing JSON
  • 2.6. Assignments
• 3. Pickle serialization
  • 3.1. What is pickle?
  • 3.2. Serialize data types
  • 3.3. Serialize sequences
  • 3.4. Serialize Dates and Datetimes
  • 3.5. Serialize and deserialize objects
  • 3.6. Serialize and deserialize to file
  • 3.7. Advanced Example
  • 3.8. Assignments
• 4. XML, XSLT, XPath
  • 4.1. xml module from standard library
  • 4.2. lxml module
  • 4.3. HTML
  • 4.4. XPATH
  • 4.5. XSLT
  • 4.6. Assignments

Database

Database

• 1. DB API
• 2. SQLite
  • 2.1. SQL Syntax
  • 2.2. Connection
  • 2.3. Execute
  • 2.4. Executemany
  • 2.5. Results
  • 2.6. Cursor
  • 2.7. Use cases
  • 2.8. Assignments
• 3. MySQL
  • 3.1. pyMySQL
• 4. PostgreSQL
  • 4.1. psycopg2
• 5. pymongo
  • 5.1. Install
  • 5.2. Insert data
  • 5.3. Select data
• 6. ORM
  • 6.1. SQLAlchemy
  • 6.2. Django ORM
• 7. Database Schema Migration
  • 7.1. SQLAlchemy
  • 7.2. Django
  • 7.3. FlywayDB
  • 7.4. LiquidBase
• 8. Data exploration
  • 8.1. Apache Superset
• 9. SQL Injection
  • 9.1. Prepare query
  • 9.2. Get user input
  • 9.3. Execute query
• 10. SQL
  • 10.1. ACID
  • 10.2. CREATE
  • 10.3. INSERT
  • 10.4. AUTOINCREMENT
  • 10.5. COMMIT and ROLLBACK
  • 10.6. SELECT
  • 10.7. UPDATE
  • 10.8. GROUP BY
  • 10.9. HAVING
  • 10.10. ALTER
  • 10.11. DROP
  • 10.12. DELETE
  • 10.13. JOIN
  • 10.14. TRUNCATE

Good Engineering Practice

Good Engineering Practice

• 1. Software Engineering Conventions
  • 1.1. PEP 8 - Style Guide for Python Code
  • 1.2. Static Code Analysis
  • 1.3. Assignment
• 2. The Zen of Python
  • 2.1. Most important rules
  • 2.2. Full Text
• 3. Code Smells
  • 3.1. Bad practice
• 4. ReST and Sphinx documentation
  • 4.1. reStructuredText vs. Markdown
  • 4.2. Format reStructuredText
  • 4.3. Sphinx
  • 4.4. Read the docs
  • 4.5. Assignments

Tests

Tests

• 1. Unit Testing
  • 1.1. Glossary
  • 1.2. Running tests
  • 1.3. Example
  • 1.4. Mock
  • 1.5. Stub
  • 1.6. Assignments
• 2. Pytest
  • 2.1. Why?
  • 2.2. Running
  • 2.3. Skip
  • 2.4. pytest.raises
  • 2.5. Fixtures

Type Annotation

Type Annotation

• 1. Type Annotation
  • 1.1. What are Type Annotations?
  • 1.2. Data types
  • 1.3. Sequences
  • 1.4. Nested sequences
  • 1.5. Union
  • 1.6. Any
  • 1.7. Optional
  • 1.8. The NoReturn type
  • 1.9. Type aliases
  • 1.10. Iterator
  • 1.11. Overload
  • 1.12. Final
  • 1.13. Literal
  • 1.14. TypedDict
  • 1.15. TypeVar, Iterable, Tuple
  • 1.16. Callable
• 2. Introspection
• 3. Type Checking
  • 3.1. MyPy
  • 3.2. PyType
  • 3.3. pyre-check
• 4. Annotating existing code
  • 4.1. PyAnnotate
  • 4.2. monkeytype

Advanced Functions

Advanced Functions

• 1. Generators
  • 1.1. Lazy evaluation
  • 1.2. Generator expressions vs. Comprehensions
  • 1.3. Conditions
  • 1.4. Returning nested objects
  • 1.5. yield Operator
  • 1.6. Example
  • 1.7. Readability counts
  • 1.8. Built-in generators
  • 1.9. Assignments
• 2. Defining function with arbitrary number of arguments
  • 2.1. Recap information about function parameters
  • 2.2. Arbitrary number of positional arguments
  • 2.3. Arbitrary number of keyword arguments
  • 2.4. Arbitrary number of positional and named arguments
  • 2.5. Keyword only
  • 2.6. Use cases
  • 2.7. Assignments
• 3. Passing many arguments
  • 3.1. Arbitrary number of positional arguments
  • 3.2. Arbitrary number of keyword arguments
  • 3.3. Arbitrary number of positional and keyword arguments
  • 3.4. Examples
  • 3.5. Assignments
• 4. Callable
  • 4.1. First-class Function
  • 4.2. Callable
  • 4.3. Aliases
  • 4.4. Assignments
• 5. Closures
  • 5.1. Nested Function
  • 5.2. What is closure?
• 6. Decorator
  • 6.1. Definition
  • 6.2. Zastosowanie
  • 6.3. Example
  • 6.4. Class Decorators
  • 6.5. functools
  • 6.6. Przykład
  • 6.7. Decorator library
  • 6.8. Assignments

Regular Expressions

Regular Expressions

• 1. Syntax
  • 1.1. What are Regular Expressions?
  • 1.2. Constructing
  • 1.3. Visualizing RegExps
  • 1.4. Qualifiers
  • 1.5. Flags
• 2. Match, search and Findall
  • 2.1. re.match()
  • 2.2. re.search()
  • 2.3. re.findall() and re.finditer()
  • 2.4. Comparision
  • 2.5. Good practices
  • 2.6. Assignments
• 3. Regex pattern compilation
  • 3.1. Without compilation
  • 3.2. With compilation
• 4. Groups and Variables
  • 4.1. RegEx parameters (variables)
• 5. Multi line searches
• 6. Substitute and Split
  • 6.1. re.sub()
  • 6.2. re.split()
  • 6.3. Assignments
• 7. Greedy and non-greedy search
• 8. Examples
  • 8.1. National Identification Numbers (Worldwide)
  • 8.2. Finding all Adverbs
  • 8.3. Making a Phonebook
  • 8.4. Writing a Tokenizer
• 9. Standards
  • 9.1. W3C HTML5 Standard regexp for email field
  • 9.2. W3C standard for URL understanding
  • 9.3. W3C standard for URL parsing
  • 9.4. Parsing URLs

Debugging

Debugging

• 1. Python WAT?!
• 2. Logging
  • 2.1. Korzystanie z logging
  • 2.2. Konfiguracja formatowania logów
  • 2.3. Rotate
  • 2.4. 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. Advanced Debugging
  • 5.1. json.tool
  • 5.2. Using pdb
  • 5.3. breakpoint()
  • 5.4. Using debugger in IDE
  • 5.5. Debugging i Wątki
  • 5.6. Debugging i Procesy
  • 5.7. Debugging aplikacji sieciowych
  • 5.8. Assignment
• 6. Introspection
  • 6.1. Introspecting Types
  • 6.2. Introspecting Objects
  • 6.3. Introspecting Docstrings
  • 6.4. Example

OOP Paradigm

OOP Paradigm

• 1. Static and Dynamic Fields
  • 1.1. Static Fields
  • 1.2. Dynamic Fields
  • 1.3. Static vs. Dynamic Fields
• 2. Inheritance vs. Composition
  • 2.1. Problem with inheritance
  • 2.2. Multi level inheritance problem
  • 2.3. Composition using Mixin classes
• 3. Method Resolution Order
  • 3.1. Inheritance Method Resolution
  • 3.2. Inheritance Diamond
• 4. Relations
  • 4.1. Relations
  • 4.2. Assignments
• 5. Polymorphism
  • 5.1. Switch
  • 5.2. Polymorphism on Function
  • 5.3. Polymorphism on Classes
• 6. Dynamic Typing
  • 6.1. Duck typing
  • 6.2. Everything is an object
  • 6.3. Monkey Patching
• 7. New and Old style classes
  • 7.1. Rationale
• 8. Interface
  • 8.1. What it is?
  • 8.2. When use it?
  • 8.3. Example
• 9. Abstract classes
  • 9.1. What it is?
  • 9.2. When use it?
  • 9.3. Abstract classes and methods
• 10. Good Engineering Practises
  • 10.1. Tell - don't ask
  • 10.2. Objects and instances
  • 10.3. S.O.L.I.D.
  • 10.4. GRASP
• 11. Exam
  • 11.1. Dragon (version beta)

OOP Protocols

OOP Protocols

• 1. Iterators
  • 1.1. Protocol
  • 1.2. Mechanism
  • 1.3. Iterating over objects
  • 1.4. Own Implementation
  • 1.5. itertools
  • 1.6. Assignments
• 2. Context Managers
  • 2.1. Protocol
  • 2.2. Application
  • 2.3. Implementation
  • 2.4. Examples
  • 2.5. Contextmanager decorator
  • 2.6. Assignments
• 3. staticmethod
  • 3.1. Rationale
  • 3.2. Application
  • 3.3. Implementation
  • 3.4. Use case
  • 3.5. Example
• 4. classmethod
  • 4.1. What it is?
  • 4.2. When use it?
  • 4.3. Example
  • 4.4. Assignments
• 5. Properties
  • 5.1. Accessing fields
  • 5.2. Properties
  • 5.3. Use Cases
  • 5.4. Assignments
• 6. Setattr, Getattr
  • 6.1. Built-in Functions
  • 6.2. Protocol
  • 6.3. __setattr__()
  • 6.4. __delattr__()
  • 6.5. __getattribute__()
  • 6.6. __getattr__()
  • 6.7. hasattr()
  • 6.8. Assignments
• 7. Descriptor
  • 7.1. Protocol
  • 7.2. Rationale
  • 7.3. Builtin Descriptor Object Examples
  • 7.4. Example
  • 7.5. Case Study
  • 7.6. Assignments

OOP Advanced

OOP Advanced

• 1. Object initialization
  • 1.1. __call__()
  • 1.2. __new__()
  • 1.3. __init__()
  • 1.4. Example usage
  • 1.5. Returning values
  • 1.6. Examples
  • 1.7. Initial arguments mutability and shared state
  • 1.8. Do not run methods in __init__()
• 2. Placeholder class
  • 2.1. Proxy methods
  • 2.2. Flat Problem
  • 2.3. Nested Problem
  • 2.4. Placeholder class
• 3. Object Identity
  • 3.1. id()
  • 3.2. hash()
  • 3.3. String interning
  • 3.4. is
• 4. Slots
  • 4.1. Using
  • 4.2. __slots__ and __dict__
  • 4.3. Inheritance
• 5. Metaclass
  • 5.1. Metaclass mechanism
  • 5.2. Type and objects
  • 5.3. Example
  • 5.4. Factories
  • 5.5. Metaclass replacements
• 6. OOP Advanced Exam
  • 6.1. Dragon (version release candidate)

Design Patterns

Design Patterns

• 1. Dependency injection
• 2. Singleton
• 3. Factory
• 4. Gateway
• 5. Callback
• 6. State Machine
• 7. Gang of Four
  • 7.1. Examples
  • 7.2. Structural Design Patterns
  • 7.3. Creational Design Patterns
  • 7.4. Behavioral Design Patterns
  • 7.5. Idiomy języka programowania

Paradigms

Paradigms

• 1. Functional Programming
  • 1.1. Lambda - Anonymous functions
  • 1.2. Built-in functions
  • 1.3. functools
  • 1.4. Callback
  • 1.5. Assignments
• 2. Event Programming
  • 2.1. Implementation
  • 2.2. Przykład użycia
  • 2.3. Użycie

System and Performance

System and Performance

• 1. Operating System
  • 1.1. Commandline argument parsing
  • 1.2. Operating System
  • 1.3. pathlib
• 2. Distributing
  • 2.1. Tools
  • 2.2. Standalone Applications
  • 2.3. Distributing Packages
• 3. CI/CD
  • 3.1. CI/CD Tools
  • 3.2. CI/CD Pipelines
  • 3.3. Linters
  • 3.4. Behavioral Testing
  • 3.5. Code Style
  • 3.6. Code Coverage
  • 3.7. Internationalization
  • 3.8. DB Schema Migration
  • 3.9. Distributing
  • 3.10. Security
  • 3.11. Smoke Tests
  • 3.12. Static Code Analysis
  • 3.13. Test Automation
  • 3.14. Type Checking
  • 3.15. UI testing
  • 3.16. Mutation Testing
• 4. High Performance
  • 4.1. Performance Optimization
  • 4.2. Micro-benchmarking
  • 4.3. Profiling
  • 4.4. Compilers and interpreters
  • 4.5. Collections
  • 4.6. C Types
  • 4.7. C Modules
• 5. Concurrency
  • 5.1. Queues
  • 5.2. Threads
  • 5.3. Multiprocessing
  • 5.4. Concurrency
• 6. GUI
  • 6.1. Graphical User Interfaces with Tkinter
  • 6.2. Biblioteka Tkinter

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. Standard Library
  • 1.1. Mathematics
  • 1.2. Statistics
  • 1.3. Random Numbers
  • 1.4. Fractions
  • 1.5. Numerical Precision
  • 1.6. matplotlib
  • 1.7. complex
• 2. Tools and libs
  • 2.1. Biblioteki w zastosowaniach naukowych i inżynieryjnych
  • 2.2. Zastosowanie naukowe
  • 2.3. Jupyter
• 3. Numpy
  • 3.1. Built-ins
  • 3.2. Random numbers
  • 3.3. Data Import and Export
  • 3.4. Array Create
  • 3.5. Array Data Types
  • 3.6. Array Attributes
  • 3.7. Array Shape
  • 3.8. Array Indexing
  • 3.9. Array Slicing
  • 3.10. Array Iteration
  • 3.11. Array Rounding
  • 3.12. Array Methods
  • 3.13. Array Arithmetic
  • 3.14. Array Concatenation
  • 3.15. Array Serialize
  • 3.16. Array Statistics
  • 3.17. Array Logic
  • 3.18. Array Select
  • 3.19. Statistics
  • 3.20. Trigonometry
  • 3.21. Polynomials
  • 3.22. Linear Algebra
  • 3.23. Bibliography
• 4. Pandas
  • 4.1. Configuration
  • 4.2. Import and Export Data
  • 4.3. Data Types
  • 4.4. Date Types
  • 4.5. Series Create
  • 4.6. Series Attributes
  • 4.7. Series Indexing
  • 4.8. Series Slice
  • 4.9. Series Modification
  • 4.10. Series Arithmetic
  • 4.11. DataFrame Create
  • 4.12. DataFrame Attributes
  • 4.13. DataFrame Select
  • 4.14. DataFrame Indexing
  • 4.15. DataFrame Slicing
  • 4.16. DataFrame Sample
  • 4.17. DataFrame Accessing Values
  • 4.18. DataFrame Locate
  • 4.19. DataFrame Modification
  • 4.20. DataFrame Sort
  • 4.21. DataFrame Statistics
  • 4.22. DataFrame Group By
  • 4.23. DataFrame Aggregations
  • 4.24. DataFrame Join
  • 4.25. Workflow
  • 4.26. Example
  • 4.27. Bibliography
• 5. Data Visualization
  • 5.1. Matplotlib
  • 5.2. Matplotlib Pandas
  • 5.3. Matplotlib Charts
  • 5.4. Matplotlib Plot Lifecycle
  • 5.5. Matplotlib Examples
  • 5.6. Plotly
  • 5.7. Bokeh
  • 5.8. Seaborn
  • 5.9. Generowanie grafów

Machine Learning

Math, Data Science and ML

• 1. Machine Learning
  • 1.1. Wprowadzenie do statystyki
  • 1.2. Wprowadzenie do Machine Learning
  • 1.3. Słowniczek intuicyjny
  • 1.4. Słownik Pojęć
  • 1.5. Datasets
  • 1.6. Machine Learning Algorithms
  • 1.7. Features
  • 1.8. Classifiers
  • 1.9. scikit-learn
  • 1.10. Model Quality
  • 1.11. Decision Tree
  • 1.12. k Nearest Neighbors
  • 1.13. Bayes
  • 1.14. Linear Regression
  • 1.15. Logistic Regression
  • 1.16. Support Vector Machines
  • 1.17. Clustering
  • 1.18. Principal Component Analysis (PCA)
  • 1.19. Neural Network
  • 1.20. Convolutional Neural Network
  • 1.21. Przydatne odnośniki
  • 1.22. Notes

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

Exam

Exam

• 1. Pre-foundations
• 2. Pre-Intermediate
  • 2.1. Create
  • 2.2. Select
  • 2.3. String cleaning
  • 2.4. Iris dataset
• 3. Pre-Advanced
  • 3.1. List[tuple] to List[dict]
  • 3.2. Get elements from nested data structure
  • 3.3. /etc/hosts - parsing to List[dict]

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. References
  • 6.1. Bibliography
  • 6.2. Images
  • 6.3. Video
• 7. Copyright
  • 7.1. MIT License