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Python – Complete Python, Django, Data Science and ML Guide
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Python – Complete Python, Django, Data Science and ML Guide
By:
Overview of this book
Join us on an immersive Python programming journey, spanning over 50 hours of learning. Whether you're a novice or experienced, this course equips you with vital Python skills for careers and projects.
Starting from the basics, grasp Python's core principles and proficiency in real-world functions. As Python's popularity grows, this course readies you for the rising demand for Python developers. You'll practice hand-on examples using Python's interpreter and Visual Studio Code with Code Runner to solidify your skills.
With a focus on Data Science and Machine Learning, you'll master essential packages such as NumPy, Pandas, Matplotlib, and Scikit-learn, using the versatile Jupyter Notebook. The course extensively covers Python's fundamental aspects, spanning variables, lists, dictionaries, and venturing into advanced topics like classes, loops, modules, and creating virtual environments. The goal is to provide you with a solid Python foundation. You'll also gain insight into functional and object-oriented Python programming, making you a versatile coder.
The course is thoughtfully structured, explaining not just "how" but also "why" we use specific methods and best practices. By course end, you'll harness Python's full potential for web and mobile app development, data science, machine learning, and game creation.
Table of Contents (91 chapters)
Introduction to Python
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Installing and Using Pycharm IDE
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Basics Concepts in Python
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Introduction to Functions and Built-in Functions in python
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Code formatting and PEP8
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Expressions and Instructions
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Variables
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Data Types and structures
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Strings
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String Concatenation
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Numeric Types
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Boolean Values
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Magic Methods
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Lists
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Dictionaries
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Tuples
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Sets
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Ranges
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Working with Sequences
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Modifying Objects in Python
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Functions
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Function Arguments
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Args and kwargs in Functions
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Default Function Parameters
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Docstrings
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Callback Functions
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Global and local Variables
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Operators
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Falsy and Truthy Values
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Logical and Comparison Operators
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Lambda Functions
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Error handling
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Sequence Unpacking
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Unpacking Dictionary
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Conditional Statements
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Ternary Operator
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For-In loop
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While Loop
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For-In Expression (Comprehension)
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Generators
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Decorator Functions
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Classes and Objects
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Instance and Class method
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Magic Methods in Classes
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Classes Extension
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Classes in Practice
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Key Principles in Object-Oriented Programming (OOP)
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Modules
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Built-in Modules
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What is __name__ and __main__
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JavaScript Object Notation (JSON)
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Working with Files
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Working with zip archives
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Working with CSV Files
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Working with Dates and Times
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Generating Random Sequences and Passwords
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Math Module and Recursive Functions
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Regular Expressions
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Sending Emails
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Working with SQLite Database
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Other Built-in Modules
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Virtual Environments
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Pipenv for Virtual Environments Management
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Introduction to the Django web framework
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Creating a Django project
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Creating a Django Application
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Database and Migrations in Django
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Creating Templates in Django
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Extending Other Templates in Django
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Creating Multiple routes and View Functions
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Routing between Pages in Django
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Creating an API Django Application
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Managing Authentication for API Requests
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Django Project Refactoring and Admin Settings
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Creating Games with Pygame
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Creating a Shooter Game with Pygame
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Interaction of the Elements in the Pygame
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Game Refactoring using Classes and OOP
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Jupyter Notebook
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Jupyter Lab
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NumPy - Creating Arrays
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NumPy – Random Values
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NumPy – Examples
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Pandas – Working with DataFrames and Series
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Pandas – Random Data Working with CSV
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Pandas – Analysing CSV- Loaded DataFrames
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Matplotlib – Creating Charts
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Scikit – Learn - Machine Learning
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Machine Learning Model for Real Data
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Making Machine Learning Model More Real
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