Python - Complete Python, Django, Data Science And Ml Guide
Last updated 8/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 22.74 GB | Duration: 50h 27m
Learn the most popular Python programming language including Django, Pygame, Jupyter, Data Science and Machine Learning
What you'll learn
You will learn the basic principles of Python and learn all the main functions that are used most often in real projects and are in demand the most
You will practice all the examples that I will show throughout the course using the Python interpreter, Visual Studio Code with Code Runner
You will master all the necessary packages for Data Science and Machine Learning such as NumPy, Pandas, Matplotlib and Scikit-learn using Jupyter Notebook
You will learn the basic functionality of Python, ranging from variables, lists, dictionaries, to classes, loops, modules, and creating virtual environments
In addition, you will learn how to use functional and object-oriented approaches in Python programming.
Requirements
There are no prerequisites, all you need is a desire to learn and practice
It is advisable to study on a laptop with an external monitor, you can also use a tablet
Description
Python is the easiest programming language in the world. But at the same time, Python is a powerful tool with which you can solve a huge range of different tasks, from file processing to machine learning, data processing, game creation and web application development .Thus, having learned Python, you can choose a profession from a wide range of vacancies, or you can use Python to create your own applications and solve your own problems.This course includes many practical tasks, as well as tasks for self-fulfillment.Python is an object oriented programming language.Python is also a language with a huge amount of features, but in order to be able to code in Python, you need to UNDERSTAND the key concepts of Python. And that's what I'm going to focus on with you in this course.Before writing code and running examples, you will receive from me explanations and answers to questions WHY and WHY , and only after that HOW to write code.I will not waste your time and therefore I have created the most effective course structure. All the examples that I will explain and run are written by me before the course, but you will write and run the code yourself.All video lectures in this course are over 50 hours long , but expect to spend around 500 hours to master all the topics of the course, including self-completion of all practical tasks.In this course you will learn following key topics:Foundational Python Programming: Learn the fundamental concepts of Python programming, from data types, functions, and variables to control structures like loops and conditional statements.Object-Oriented Programming (OOP): Dive into the principles of OOP, understanding classes, objects, inheritance, encapsulation, and polymorphism, and discover how to leverage them for efficient code organization.File Handling and Modules: Explore file manipulation techniques, from working with directories and files using the os module to using external modules, enabling code reuse, and managing packages with PIP.Web Development with Django: Get an introduction to web development using Django, covering MVC architecture, URL routing, model creation, and interacting with databases to build dynamic web applications.API Development: Learn to create RESTful APIs using Django and handle API requests and responses, including authentication, authorization, and versioning.Game Development with Pygame: Enter the world of game development with Pygame, creating interactive games by working with graphics, animations, and user input.Data Manipulation with NumPy and Pandas: Discover data analysis and manipulation using NumPy and Pandas, covering array operations, dataframes, and handling real-world data sets.Error Handling: Understand error handling mechanisms in Python ensuring robust and reliable code.Package Management and Virtual Environments: Master package management using PIP, create virtual environments to isolate projects, and manage dependencies effectively.Visualization and Machine Learning: Explore data visualization with Matplotlib, and dip your toes into machine learning concepts with Scikit-Learn, covering model creation, evaluation, and prediction.Why it's important: This course provides a comprehensive foundation in Python programming, from basic syntax to advanced topics like OOP, web and game development, data manipulation, and more. Understanding these concepts is crucial for building versatile applications, performing data analysis, and even stepping into machine learning, ensuring you're equipped for a wide range of programming tasks and projects.After completing this course, you can safely say that you KNOW Python and CAN use the most popular Python functions. As any of my courses this course comes with 30-days money back guarantee. No questions asked!
Overview
Section 1: Introduction to Python
Lecture 1 Introduction to the Complete Python Guide
Lecture 2 Where to Write and Run Python Code
Lecture 3 Practice - Installing Python
Lecture 4 Practice - Using the Python Interactive Interpreter
Section 2: Installing and Using PyCharm IDE
Lecture 5 Installing PyCharm
Lecture 6 Getting Familiar with the PyCharm Interface
Section 3: Course and Project Files
Lecture 7 Download Project Files
Section 4: Basic Concepts in Python
Lecture 8 Key Concept in Python
Lecture 9 Main Data Types in Python
Lecture 10 Practice - Working with Main Data Types
Section 5: Introduction to Functions and Built-in Functions in Python
Lecture 11 Built-in Functions
Lecture 12 Practice - Defining and Using Functions
Lecture 13 Practice - Using the Return Statement in Functions
Lecture 14 Practice - Exploring Built-in Functions
Lecture 15 Practice - Using the built-in dir() Function
Lecture 16 Practice - Gathering User Input with the built-in input() Function
Section 6: Code Formatting and PEP8
Lecture 17 Code Indentations
Lecture 18 Practice - Working with Indentations
Lecture 19 Following PEP 8 Guidelines
Lecture 20 Enabling Auto-Formatting in PyCharm
Section 7: Comments
Lecture 21 Comments
Lecture 22 Practice - Adding Comments to Your Code
Section 8: Expressions and Instructions
Lecture 23 Understanding Expressions
Lecture 24 Understanding Statements
Lecture 25 Practice - Using Expressions
Lecture 26 Practice - Using Statements
Section 9: Variables
Lecture 27 Variables
Lecture 28 Practice - Defining and Using Variables
Section 10: Data Types and Structures
Lecture 29 Understanding Dynamic Typing
Lecture 30 Types and Data Structures Overview
Lecture 31 Variables and Objects
Lecture 32 Practice - Using the built-in id() Function
Lecture 33 Practice - Exploring Core Data Classes (str, int, bool, list, dict)
Lecture 34 Practice - Using the built-in isinstance() Function
Section 11: Strings
Lecture 35 Strings
Lecture 36 Practice - String Manipulation
Lecture 37 Practice - String Methods
Section 12: String Concatenation
Lecture 38 String Concatenation
Lecture 39 Practice - Concatenating Strings using the + Operator
Lecture 40 Practice - Using f-strings for String Formatting
Lecture 41 Practice - Alternative String Formatting Methods
Section 13: Numeric Types
Lecture 42 Integers
Lecture 43 Practice - Integers Manipulation
Lecture 44 Float Numbers
Lecture 45 Practice - Floating-Point Numbers Manipulation
Lecture 46 Working with Complex Numbers
Section 14: Boolean Type
Lecture 47 Boolean Values
Lecture 48 Practice - Working with Boolean Values
Lecture 49 Type Conversion
Section 15: Magic Methods
Lecture 50 Magic Methods
Lecture 51 Practice - Utilizing Magic Attributes and Methods
Section 16: Lists
Lecture 52 Lists
Lecture 53 List Methods
Lecture 54 Practice - Working with Lists
Lecture 55 Copying Lists
Lecture 56 Practice - Copying Lists
Lecture 57 TASK - Working with Lists
Section 17: Dictionaries
Lecture 58 Dictionaries
Lecture 59 Practice - Manipulating Dictionaries
Lecture 60 Practice - Dictionary Methods
Lecture 61 Other Operations with Dictionaries
Lecture 62 Practice - Using the get() Method for Dictionaries
Lecture 63 Practice - Converting Other Types to a Dictionary
Lecture 64 TASK - Working with Dictionaries
Section 18: Tuples
Lecture 65 Tuples
Lecture 66 Practice - Tuples Manipulation
Section 19: Sets
Lecture 67 Sets
Lecture 68 Practice - Working with Sets
Lecture 69 Understanding Set Theory
Lecture 70 Set Methods
Lecture 71 Practice - Usage of the Set Methods
Lecture 72 Practice - Calculating Symmetric Difference of Sets
Lecture 73 TASK - Working with Sets
Section 20: Ranges
Lecture 74 Ranges
Lecture 75 Practice - Range Manipulation
Lecture 76 Practice - Range Methods and Attributes
Section 21: Working with Sequences
Lecture 77 Built-in Functions for Sequences
Lecture 78 Built-in zip() Function
Lecture 79 Practice - Working with zip Objects
Lecture 80 Practice - Converting a zip Object to a Dictionary
Lecture 81 Comparison of Different Sequences
Section 22: Modifying Objects in Python
Lecture 82 Understanding Immutable Objects in Python
Lecture 83 Understanding Mutable Objects in Python
Lecture 84 Strategies to Prevent Object Mutation
Lecture 85 Practice - Creating Deep Copies of Objects
Section 23: Functions
Lecture 86 Functions
Lecture 87 Calling Functions: Arguments vs Parameters
Lecture 88 Shortest Function in Python
Section 24: Function Arguments
Lecture 89 Mutable and Immutable Arguments in Function Calls
Lecture 90 Practice - Using Mutable and Immutable Objects as Function Arguments
Lecture 91 Practice - Mandatory and Optional Positional Arguments
Lecture 92 TASK - Functions Manipulation
Lecture 93 Function Arguments
Section 25: Args and kwargs in Functions
Lecture 94 Practice - Using *args to Gather Positional Arguments into a Tuple
Lecture 95 Keyword Arguments
Lecture 96 Practice - Working with Keyword Arguments
Lecture 97 Practice - Using **kwargs to Merge Keyword Arguments in a Dictionary
Lecture 98 TASK - Manipulating Function Arguments
Lecture 99 Args and kwargs
Lecture 100 Practice - Gathering Positional Arguments into the *args Tuple
Lecture 101 Practice - Gathering All Keyword Arguments into the **kwargs Dictionary
Section 26: Default Function Parameters
Lecture 102 Default Function Parameters
Lecture 103 Practice - Using Default Function Parameters
Section 27: Docstrings
Lecture 104 Docstrings
Lecture 105 Practice - Writing and Using Docstrings
Lecture 106 Practice - Exploring Docstrings
Lecture 107 Practice - Adding Docstrings to Functions
Section 28: Callback Functions
Lecture 108 Callback Functions
Lecture 109 Rules for Working with Functions
Section 29: Global and Local Variables
Lecture 110 Scopes
Lecture 111 The Global Keyword
Lecture 112 Practice - Global and Local Variables
Lecture 113 Practice - Using the Global Keyword
Section 30: Operators
Lecture 114 Operators
Lecture 115 Unary and Binary Operators
Lecture 116 Practice - Working with Prefix Unary Operators
Lecture 117 TASK - Operators
Section 31: Falsy and Truthy Values
Lecture 118 Falsy and Truthy Values
Lecture 119 Practice - Falsy and Truthy Values
Section 32: Logical and Comparison Operators
Lecture 120 Logical Operators
Lecture 121 Practice - Short-Circuit OR Operator
Lecture 122 Practice - Short-Circuit AND Operator
Lecture 123 Practice - Combining OR and AND Operators
Lecture 124 Practice - Examples with Logical Operators
Lecture 125 Practice - Comparison Operators
Lecture 126 The del Statement
Section 33: Lambda Functions
Lecture 127 Lambda Functions
Lecture 128 Practice - Returning Lambda Functions from Functions
Lecture 129 Practice - Sorting a List using Lambda Functions
Lecture 130 Practice - Filtering a List using Lambda Functions
Section 34: Error Handling
Lecture 131 Error Handling
Lecture 132 Practice - Using Different Error Classes in the Try and Except
Lecture 133 Practice - Using Multiple Error Classes in one Except Block and Parent Exception
Lecture 134 Practice - Using Else and Finally Blocks
Lecture 135 Example - Handling File Not Found Errors
Lecture 136 Example - Handling Undefined Variable Errors
Lecture 137 Practice - Raising Custom Errors
Lecture 138 Practice - Handling Raised Errors using Try and Except
Lecture 139 Practice - Specifying Types for Function Parameters
Lecture 140 TASK - Proper Error Handling
Section 35: Sequence Unpacking
Lecture 141 Sequence Unpacking
Lecture 142 Practice - Unpacking Tuples
Lecture 143 Practice - Unpacking a List of Tuples
Lecture 144 Practice - Unpacking Remaining Elements
Lecture 145 Practice - Unpacking Selected Elements
Lecture 146 Practice - Unpacking a List into Positional Arguments
Lecture 147 Practice - Unpacking a Dictionary into Keyword Arguments
Lecture 148 Practice - Flexibility in Function Calls
Section 36: Unpacking Dictionaries
Lecture 149 Dictionary Unpacking Operator **
Lecture 150 Practice - Using the Dictionary Unpacking Operator
Lecture 151 Practice - Merging Two Dictionaries
Section 37: Conditional Statements
Lecture 152 Conditional Statements
Lecture 153 Practice - Working with Multiple if Statements
Lecture 154 The if-else Statement
Lecture 155 The if-elif Statement
Lecture 156 Practice - Combining if, elif, and else Statements
Lecture 157 Practice - Considering the Order of Conditions in if Statements
Lecture 158 Practice - Incorporating if Statements into Functions
Lecture 159 Practice - Using if and return Statements within Functions
Lecture 160 Example - Calculating School Grades using if and return in the Function
Lecture 161 TASK - Conditional Statements
Section 38: Ternary Operator
Lecture 162 Ternary Operator
Lecture 163 Practice - Utilizing the Ternary Operator
Lecture 164 Example - Calculating Discounts with the Ternary Operator
Lecture 165 Example - Data Manipulation using the Ternary Operator
Lecture 166 Example - Calculating School Grades using the Ternary Operator
Section 39: For-In Loop
Lecture 167 Loops
Lecture 168 For-In Loop
Lecture 169 Practice - Iterating through Lists and Tuples using For-In Loops
Lecture 170 Practice - Iterating through Dictionaries using For-In Loops
Lecture 171 Practice - Iterating through Ranges, Strings, and Sets with For-In Loops
Lecture 172 TASKS - Working with For-In Loops
Section 40: While Loop
Lecture 173 While Loop
Lecture 174 Practice - Utilizing the While Loop
Lecture 175 Example - Making Selections with the While Loop
Lecture 176 Practice - Using break Statements in While and For-In Loops
Lecture 177 Practice - Using continue and break Statements in While Loops
Lecture 178 TASK - While Loop
Section 41: For-In Expression (Comprehensions)
Lecture 179 For-In Expression
Lecture 180 List, Set, and Dictionary Comprehensions
Lecture 181 Practice - Using List Comprehension
Lecture 182 Practice - Using Dictionary Comprehension
Lecture 183 Practice - Utilizing Tuple Comprehension
Lecture 184 Practice - Converting Tuples to Lists
Lecture 185 Example - Constructing Dictionaries from Sequences
Lecture 186 Practice - Short For-In Loops with Conditional Statements
Lecture 187 Example - Converting Dictionary to Another Dictionary
Lecture 188 TASKS - Short For-In Loops
Lecture 189 Example - Chaining For-In Expressions
Section 42: Generators
Lecture 190 Generators in For-In Expressions
Lecture 191 Practice - Generators and Iteration over the Generator
Section 43: Decorator Functions
Lecture 192 Introduction to Decorator Functions
Lecture 193 Example - Verifying User Permissions with Decorator Functions
Lecture 194 Example - Logging using Decorator Functions
Lecture 195 Example - Validating Arguments with Decorator Functions
Section 44: Objects and Classes
Lecture 196 Classes and Objects
Lecture 197 Practice - Understanding Classes and Class Instances
Lecture 198 Practice - Adding Instance Attributes through Dot Notation
Lecture 199 Adding Instance Attributes using the __init__ Method
Lecture 200 Practice - Incorporating Own Instance Attributes with the __init__ Method
Section 45: Instance and Class Methods
Lecture 201 Instance vs Class Methods
Lecture 202 Practice - Inheriting Methods by the Instances
Lecture 203 Static Class Methods
Lecture 204 Practice - Utilizing Static Methods in Classes
Lecture 205 Class Attributes
Lecture 206 Practice - Working with Class Attributes
Section 46: Magic Methods in Classes
Lecture 207 Magic Methods in Classes
Lecture 208 Practice - Utilizing Magic Methods in Classes
Section 47: Classes Extension
Lecture 209 Inheritance from Other Classes
Lecture 210 Practice - Extending Classes
Section 48: Classes on Practice
Lecture 211 Example - Creating Forum, User, and Post Classes
Lecture 212 Example - Creating Instances of the Forum, User, and Post Classes
Lecture 213 Example - Methods for Finding Users by Username and Email
Lecture 214 Example - Method for Finding All Posts by a Specific User
Lecture 215 Example - Retrieving User Posts by Email
Lecture 216 Example - Adding Parameter Types
Lecture 217 Example - Wrapping up the Forum, Users, and Posts Example
Section 49: Key Principles in Object-Oriented Programming
Lecture 218 Encapsulation in Object-Oriented Programming (OOP)
Lecture 219 Inheritance in Object-Oriented Programming (OOP)
Lecture 220 Polymorphism in Object-Oriented Programming (OOP)
Lecture 221 Abstraction in Object-Oriented Programming (OOP)
Section 50: Modules
Lecture 222 Modules
Lecture 223 Practice - Importing Entire Custom Modules
Lecture 224 Practice - Selective Imports from Other Modules
Lecture 225 Practice - Importing between Different Modules
Lecture 226 Practice - Modules in Subfolders
Section 51: Built-in Modules
Lecture 227 Built-in Modules
Lecture 228 Practice - Importing from Built-in Modules
Section 52: What is __name__ and __main__
Lecture 229 Practice - __name__ and __main__
Lecture 230 Example - Executing Functions only when Module is run Directly
Lecture 231 Practice - Packages in Python
Section 53: JavaScript Object Notation (JSON)
Lecture 232 JavaScript Object Notation (JSON)
Lecture 233 Practice - Converting Python Objects to JSON
Lecture 234 Practice - Converting from JSON to Python Objects
Lecture 235 Practice - Formatting Dictionaries using JSON
Lecture 236 TASKS - JSON
Section 54: Working with Files
Lecture 237 Working with Files
Lecture 238 Working with Files and Directories using the os Module
Lecture 239 Removing Files and Directories using the os Module
Lecture 240 Summary of Directory and File Operations using the os Module
Lecture 241 Working with Files and Directories using the Path Class
Lecture 242 Iterating over Directories and Removing Files using the Path Class
Lecture 243 Reading and Writing Files
Lecture 244 Writing and Reading Files using the built-in open Function
Lecture 245 Using the with Statement
Lecture 246 Removing Files using unlink
Lecture 247 TASK - Files
Section 55: Working with Zip Archives
Lecture 248 Built-in zipfile Module and Creating Zip Archives
Lecture 249 Reading from the Zip Archive
Section 56: Working with CSV Files
Lecture 250 Working with CSV Files
Lecture 251 Iterating over Each Row in the CSV File
Section 57: Working with Dates and Times
Lecture 252 Built-in datetime Module
Lecture 253 Examples - Using the datetime Class
Lecture 254 Examples - Converting Strings to Datetime Objects
Lecture 255 Example - Working with the timedelta Class
Lecture 256 Built-in time Module
Section 58: Generating Random Sequences and Passwords
Lecture 257 Built-in random Module
Lecture 258 Examples - Utilizing choices and shuffle Methods from the random Module
Lecture 259 Built-in secrets Module
Lecture 260 Examples - Generating CSRF Tokens, URL-Safe Tokens, and OTP Passwords
Lecture 261 Example - Generating Strong Passwords
Section 59: Math Module and Recursive Functions
Lecture 262 Built-in math Module
Lecture 263 Recursive Functions
Section 60: Regular Expressions
Lecture 264 Built-in re Module for Regular Expressions
Lecture 265 Example - Creating Patterns for Matching
Lecture 266 Example - Email Validation using Regular Expressions
Lecture 267 Example - Substring Replacement using Regular Expressions
Lecture 268 Example - Removing Excessive Spaces using Regular Expressions
Lecture 269 TASK - Password Verification
Section 61: Sending Emails
Lecture 270 Running smtp4dev SMTP server in a Docker Container
Lecture 271 Sending an Email using SMTP
Lecture 272 Formatting an Email using an HTML Template
Lecture 273 SMTP Wrap-Up and Removing the Docker smtp4dev Container
Section 62: Working with SQLite Database
Lecture 274 Creating an SQLite3 Database and Table
Lecture 275 Writing Data into the SQLite Table
Lecture 276 Reading Data from the SQLite Table
Lecture 277 SQLite Summary
Section 63: Other Built-in Modules
Lecture 278 Built-in array Module
Lecture 279 Saving Arrays to Files and Reading Arrays from Files
Lecture 280 Accessing Program Arguments using the built-in sys Module
Lecture 281 Built-in webbrowser Module
Section 64: Virtual Environments
Lecture 282 Introduction to PIP - Package Manager for Python
Lecture 283 Using a Globally Installed requests Package
Lecture 284 Uninstalling Globally Installed Packages using PIP
Lecture 285 Creating a Python Virtual Environment
Lecture 286 Activation and Deactivation of the Virtual Environment in the Shell
Lecture 287 Installing Packages within the Virtual Environment
Lecture 288 Saving a List of Installed Packages in a Requirements Text File
Lecture 289 Challenges of Package Management using Requirements Files
Section 65: Pipenv for Virtual Environments Management
Lecture 290 Installing pipenv for Virtual Environments Management
Lecture 291 Creating a Virtual Environment using pipenv
Lecture 292 Installing Packages using pipenv
Lecture 293 Updating Packages using pipenv
Lecture 294 Recreating Virtual Environment in the Project Folder using pipenv
Lecture 295 Using venv for Virtual Environments in PyCharm
Lecture 296 Using pipenv for Virtual Environments in PyCharm
Section 66: Introduction to the Django Web Framework
Lecture 297 Introduction to the Django Web Framework and Project Overview
Lecture 298 Model View Controller (MVC) Programming Pattern
Lecture 299 Understanding How MVC Pattern is Implemented in Django
Lecture 300 Creating a New PyCharm Project and Installing Django
Section 67: Creating a Django Project
Lecture 301 Creating a New Django Project
Lecture 302 Overview of the manage.py File in Django
Lecture 303 Starting and Verifying the Django Server
Lecture 304 Overview of Settings in the Django Project
Lecture 305 Overview of Default Routing Configuration in Django
Section 68: Creating a Django Application
Lecture 306 Creating the Shop Application in Django
Lecture 307 Explaining the Naming of the Django Project as "base"
Lecture 308 Exploring the Contents of the Shop Application
Lecture 309 Creating a View Function
Lecture 310 Attaching the View Function to a URL
Lecture 311 Adding Shop Application Routes to the Global Project Routing Configuration
Section 69: Database and Migrations in Django
Lecture 312 Applying Default Migrations in the Django Project
Lecture 313 Creating an Admin User in the Django Project
Lecture 314 Creating Course and Category Models
Lecture 315 Enabling the Shop Application in the Django Project
Lecture 316 Creating and Applying Migrations for the Shop Application
Lecture 317 Modifying Database Models
Lecture 318 Creating a Category using the Category Model in the Shell
Lecture 319 Creating Courses using the Course Model in the Shell
Lecture 320 Creating Categories and Courses in the Admin Interface
Lecture 321 Modifying How Courses and Categories are Displayed in the Admin Panel
Lecture 322 Sending Course Titles to the Client in the Response
Section 70: Creating Templates in Django
Lecture 323 Creating an HTML Template
Lecture 324 Using an HTML Template in the View Function
Lecture 325 Populating the HTML Template with Data from the Database
Lecture 326 How we Connected Templates, Views, and Models
Lecture 327 Adding the Bootstrap CSS Library to the HTML Template
Section 71: Extending Other Templates in Django
Lecture 328 Creating a Base HTML Template for Reuse in Other Templates
Lecture 329 Adding a Navigation Bar in the Base Template
Lecture 330 TASK - Making the Title of the Web Page Dynamic
Lecture 331 SOLUTION - Making the Title of the Web Page Dynamic
Section 72: Creating Multiple Routes and View Functions
Lecture 332 Creating a Route for the Single Course Web Page
Lecture 333 Creating a View Function for the Single Course
Lecture 334 TASK - Creating an HTML Template for the Single Course
Lecture 335 SOLUTION - Creating an HTML Template for the Single Course
Lecture 336 Responding with a 404 When Course is Not Found in the Database
Section 73: Routing Between Pages in Django
Lecture 337 Setting Up Routing Between Pages Using Relative or Absolute Paths
Lecture 338 Setting Up Routing Based on the Names of the URL Patterns
Lecture 339 Considering Application Names in the Routing Setup
Lecture 340 Adding a Link to the All Courses Page
Lecture 341 Moving the Templates Folder Out of the Shop Application Folder
Lecture 342 Modifying the Model for the Courses
Lecture 343 Summary of the Django Shop Application
Lecture 344 Installing django-tastypie for the API Django Application
Section 74: Creating an API Django Application
Lecture 345 Creating an API Django Application
Lecture 346 Creating Models for the API Application
Lecture 347 Configuring Routing for the API Application
Lecture 348 Verifying the API Service
Lecture 349 Adding Version for the API
Lecture 350 Installing Postman and Sending GET and DELETE Requests
Section 75: Managing Authentication for API Requests
Lecture 351 Creating an API Key for the User
Lecture 352 Enabling Authentication and Authorization for the Model and Using DELETE Method
Lecture 353 Disabling Authentication Only for GET Requests
Lecture 354 Creating a New Resource Using POST Method
Lecture 355 Properly Connecting the Course to the Category in POST Requests Using Hydrate Me
Lecture 356 Adding Dehydrate Method to Modify Data Before Sending to Client
Lecture 357 Summary for Setting Up GET, POST, and DELETE Requests
Section 76: Django Project Refactoring and Admin Settings
Lecture 358 Refactoring Routing for the API Application
Lecture 359 Setting Up Index Route and Adding Navigation to Navbar
Lecture 360 Modifying Administrative Panel
Lecture 361 Summary of Django Courses Project
Section 77: Creating Games with Pygame
Lecture 362 Introduction to Pygame and Creating the Game Window
Lecture 363 Modifying Background Color of the Game Surface
Lecture 364 Displaying a Rectangle in the Game
Lecture 365 TASK - Placing Rectangle in the Middle of the Game Window
Lecture 366 SOLUTION - Placing Rectangle in the Middle of the Game Window
Lecture 367 Moving Rectangle Using Keyboard Arrows
Lecture 368 Stopping Rectangle from Moving Outside of the Surface
Section 78: Creating a Shooter Game with Pygame
Lecture 369 Final Shooter Game Overview
Lecture 370 Loading Images for the Game and Fighter
Lecture 371 Displaying Fighter on the Surface
Lecture 372 Moving Fighter Left or Right
Lecture 373 Making Fighter Movement Continuous
Lecture 374 Adding the Ball to the Game
Lecture 375 Showing Ball Based on Fighter Position
Lecture 376 Moving the Ball After Firing
Lecture 377 Adding the Alien to the Game
Lecture 378 Moving the Alien Down the Surface
Section 79: Interaction of the Elements in the Pygame
Lecture 379 Detecting Collision Between Alien and Fighter, Ending the Game
Lecture 380 Hitting the Alien with the Ball
Lecture 381 Increasing Alien Speed After Each Hit
Lecture 382 Adding Hit Counter
Lecture 383 Shooter Game Summary
Section 80: Game Refactoring using Classes and OOP
Lecture 384 Start of Shooter Refactoring and Creating the Fighter Class
Lecture 385 Adding Methods in the Fighter Class
Lecture 386 Creating an Alien Class
Lecture 387 Adding Methods in the Alien Class
Lecture 388 Creating a Ball Class
Lecture 389 Adding Methods in the Ball Class
Lecture 390 Creating a Game Class
Lecture 391 Adding Methods in the Game Class
Lecture 392 Adding Methods for Drawing Elements and Finalizing Refactoring
Lecture 393 Game Refactoring Summary
Lecture 394 Running the Game After Refactoring
Section 81: Jupyter Notebook
Lecture 395 Installing Jupyter Notebook
Lecture 396 Editing in Jupyter Notebook
Lecture 397 Order of Execution of Cells in Jupyter Notebook
Lecture 398 Adding Markdown, Saving, and Loading Jupyter Notebooks
Section 82: Jupyter Lab
Lecture 399 Installing Jupyter Lab and Editing Notebooks
Lecture 400 Exploring Features of Jupyter Lab
Lecture 401 Installing External Packages in Jupyter Notebook
Section 83: NumPy - Creating Arrays
Lecture 402 Introduction to NumPy and Creating One-Dimensional Arrays
Lecture 403 Two-Dimensional Arrays in NumPy
Lecture 404 Understanding Axes in NumPy
Lecture 405 Arithmetic Operations with NumPy Arrays
Lecture 406 Concatenating NumPy Arrays
Lecture 407 Summary of Basic Operations with NumPy Arrays
Section 84: NumPy - Random Values
Lecture 408 Filling a NumPy Array with Zeroes, Ones, or Random Floats
Lecture 409 Generating Random Elements Using randint and uniform
Lecture 410 Understanding Seed Number
Lecture 411 NumPy arange, reshape, and flatten Methods
Section 85: NumPy - Examples
Lecture 412 NumPy Examples 1 and 2 (One-Dimensional Array)
Lecture 413 NumPy Examples 3 and 4 (One-Dimensional Array)
Lecture 414 NumPy Example 5 (Two-Dimensional Array)
Lecture 415 NumPy Example 6 (Two-Dimensional Array)
Lecture 416 NumPy Example 7 (Three-Dimensional Array)
Lecture 417 NumPy Summary
Section 86: Pandas - Working with DataFrames and Series
Lecture 418 Introduction to Pandas and Installation
Lecture 419 Creating a DataFrame from a Dictionary
Lecture 420 Basic Operations with DataFrame
Lecture 421 Describing the DataFrame
Lecture 422 Finding Null Values in the DataFrame
Lecture 423 Finding Columns with Specific Data Type
Lecture 424 Series Data Structure in Pandas
Lecture 425 Selecting Part of the DataFrame Using loc and iloc Properties
Lecture 426 Filtering Data in the DataFrame
Lecture 427 Datetime Type in Pandas
Lecture 428 Sorting Data in the DataFrame
Lecture 429 Adding and Removing Columns and Concatenating DataFrames
Lecture 430 Summary of Pandas DataFrames and Series
Section 87: Pandas - Random Data and Working with CSV
Lecture 431 Generating Random Data for DataFrames
Lecture 432 Creating a DataFrame Using Random Data
Lecture 433 Saving DataFrames to CSV Files
Lecture 434 Creating DataFrames from CSV Files
Lecture 435 Writing DataFrames to Excel and JSON Files
Section 88: Pandas - Analysing CSV-Loaded DataFrames
Lecture 436 Analyzing CSV-Loaded DataFrames
Lecture 437 Grouping Data in DataFrames
Lecture 438 Displaying Series Data on Plots Using Matplotlib
Lecture 439 Summary of Example with Random CSV Data
Section 89: Matplotlib - Creating Charts
Lecture 440 Examples of Plot and Scatter Diagrams Using Matplotlib
Lecture 441 Examples of Matplotlib Subplots
Lecture 442 Using DataFrames for Creating Diagrams
Lecture 443 Boxplots, Area Plots, and Pie Charts
Lecture 444 Example of a Heatmap in Matplotlib
Lecture 445 Displaying Real-World Data on Various Charts
Section 90: Scikit-learn - Machine Learning
Lecture 446 Introduction to Scikit-Learn and Installation
Lecture 447 Loading and Analyzing Sample Data for Model Creation
Lecture 448 Handling Null Values in DataFrame
Lecture 449 Attempting to Create a Model for Predicting Target Values
Lecture 450 Encoding Non-Numeric Values in Input Data
Lecture 451 Building and Predicting with Cleaned and Encoded Data
Lecture 452 Summary of Model for Predicting Favorite Transport
Lecture 453 Visualizing DecisionTreeClassifier Model
Lecture 454 Creating Charts for Data from the Built Model
Lecture 455 Evaluating Model Accuracy
Section 91: Machine Learning Model for Real Data
Lecture 456 Loading CSV File with Airline Passenger Satisfaction Data
Lecture 457 Analyzing DataFrame with Passenger Satisfaction Data
Lecture 458 Filling Null Values with Mean Value
Lecture 459 Creating Diagrams for Passenger Data Analysis
Lecture 460 Manually Encoding Non-Numeric Values in DataFrame
Lecture 461 Encoding Non-Numeric Values Using LabelEncoder
Lecture 462 Creating Additional Diagrams After Data Cleaning and Encoding
Lecture 463 Filtering DataFrame with Passenger Data
Lecture 464 Using DecisionTreeClassifier for Model Creation
Lecture 465 Measuring Model Accuracy with DecisionTreeClassifier
Lecture 466 Using Other Classifiers for Model Creation
Lecture 467 Summary of Airline Passenger Satisfaction Project
Section 92: Making Machine Learning Model More Real
Lecture 468 Removing Passenger Votes from DataFrame
Lecture 469 Saving Trained Model for Future Use
Lecture 470 Summary of Realistic Model for Passenger Satisfaction Prediction
Beginning Python programmers who want to learn how to program,Those who are planning to work in the direction of Data Science and Machine Learning,Web developers who want to build web applications with Python,Those who want to perform tasks related to machine learning, data processing,Game developers who want to create games with Python Pygame
rompt EngineeringGenerative AIAI Text Content GenerationChatGPT IntensiveGoogle Bard IntensiveMidjourney IntensiveOpen AIDALL-E 2What can ChatGPT, Google Bard, & Midjourney do for you? Here are some examples we'll be covering in this ChatGPT Masters course.AI For Content CreationFind Keywords for Future or Existing ContentCreate Outlines for ContentWrite Entire Articles and Blog PostsGenerate Title IdeasAI For Social MediaResearch Ideas for Catchy PostsWrite Copy for Instagram, Facebook, Pinterest, YouTube, etc postsGenerate PollsMake Lists - Fun Facts, Stats, Jokes, QuotesAI For Digital Marketing & BusinessCreate a complete content calendarDefine your Target audience and buyer personaSocial Media PostsWriting blogs and articles + SEOEmail Marketing - Email sequencesWrite a Sales PageWrite a Facebook AdWrite a Google AdCondense Customer FeedbackWebsite creationBranding & E-commerceAI for Marketing FunnelsIdentify your buyer persona with ChatGPTCreating a lead magnet with ChatGPTLanding page and social media postEmail sequence for the funnelAI For Video Creators and YouTubersCreate video outlinesWrite video scriptsCorrect and improve subtitlesCome up with catchy YouTube titlesAI For PodcastersCome Up with a Great Podcast NameResearch Episode ideasWrite an Episode OutlineCreate Interview QuestionsWrite Episode TitlesFind Possible GuestsAI For Online Course CreatorsCome Up with Your Next Course IdeaWrite a Course OutlineWrite a Course Description / Landing PageWrite a Promo Video ScriptWrite Course Promotional EmailsWrite Course Promotional Social Media PostsThe course will be divided into sections, including research, content creation, social media, video/YouTube, websites, branding, marketing, office productivity, e-commerce and podcasting. In each section, you'll learn how to use ChatGPT for various tasks, such as finding keywords, creating outlines, writing articles, generating title ideas, and developing marketing strategies.By the end of this course, you'll have a comprehensive understanding of ChatGPT, Google Bard & Midjournehy and how to use these AI tools for your specific needs. Whether you're a business owner, marketer, content creator, or student, you can start using ChatGPT, Google Bard & Midjourney today and take your work and business to the next level.We'll continue to update this course as OpenAI releases new GPT updates, such as the recent GPT-4 update that will include multimodal AI generation. We'll be adding new lessons on other AI tools that are released as well!See you in class!Phil &DiegoMore info about how the ChatGPT Masterclass is structured:The course begins with an introduction to ChatGPT, Google Bard, and Midjourney, offering numerous ideas and prompts for using ChatGPT and Google Bard effectively. You'll also learn about the latest GPT-4 release and how it compares to GPT-3.5.The following sections dive deep into using ChatGPT for research, content creation, social media, digital marketing, marketing funnels, video creation and YouTube, office productivity, branding and e-commerce, customer feedback analysis, podcasting, and essay writing. You'll also explore the ethical considerations of using ChatGPT in these areas.Next, you'll be introduced to Google Bard, with lessons on how to use the AI tool for research, content marketing, social media, video creation and YouTube, podcasting, online course creation, digital marketing, office productivity, branding and e-commerce, and customer feedback analysis. Plus, learn how to write ethical essays with Google Bard.One section features a real-world demonstration of ChatGPT, Google Bard, generative AI, and prompt engineering, showcasing a speed comparison between the AI and a human.In the following sections, you'll learn how to use Midjourney, an AI tool for creating unique images in seconds, to develop business concepts, create product images and photographs, generate website ideas, and design logos. You'll also explore advanced options and tips for creating better prompts with Midjourney.Finally, the course concludes, leaving you equipped with the skills and knowledge to use ChatGPT, Google Bard, and Midjourney to their full potential in your personal and professional life.By the end of this course, you'll be an expert in using these powerful AI tools to enhance your content creation, marketing, and productivity efforts. Sign up today to unlock the full potential of ChatGPT, Google Bard, and Midjourney!
