HarvardX: CS50's Introduction to Programming with Python (Free Course)

 

An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more. 

About this course

An introduction to programming using a language called Python. Learn how to read and write code as well as how to test and "debug" it. Designed for students with or without prior programming experience who'd like to learn Python specifically. Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs, and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files. Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems. No software required except for a web browser, or you can write code on your own PC or Mac.

Whereas CS50x itself focuses on computer science more generally as well as programming with C, Python, SQL, and JavaScript, this course, aka CS50P, is entirely focused on programming with Python. You can take CS50P before CS50x, during CS50x, or after CS50x. But for an introduction to computer science itself, you should still take CS50x!

What you'll learn

1. Functions, Variables
2. Conditionals
3. Loops
4. Exceptions
5. Libraries
6. Unit Tests
7. File I/O
8. Regular Expressions
9. Object-Oriented Programming
10. Et Cetera

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What is the difference between deep copy and shallow copy in Python, and how can you create each type for a nested list?

In Python, a deep copy and a shallow copy are two different ways to duplicate a list (or any mutable object) with potentially different behaviors when it comes to nested objects. Here's the difference between them and how you can create each type for a nested list:

Shallow Copy:

A shallow copy creates a new object, but it doesn't create copies of the objects within the original object. Instead, it copies references to those objects. This means that changes made to objects inside the copied list will also affect the original list and vice versa if those objects are mutable.

You can create a shallow copy using the copy module's copy() function or by using the slicing [:] notation.

Example of creating a shallow copy:

import copy

original_list = [[1, 2, 3], [4, 5, 6]]

shallow_copied_list = copy.copy(original_list)

# Now, both original_list and shallow_copied_list share the same sublists.

shallow_copied_list[0][0] = 100

print(original_list)  # Output: [[100, 2, 3], [4, 5, 6]]

Deep Copy:

A deep copy, on the other hand, creates a completely independent copy of the original object and all objects contained within it, recursively. This means changes to objects within the copied list won't affect the original list and vice versa.

You can create a deep copy using the copy module's deepcopy() function.

Example of creating a deep copy:

import copy

original_list = [[1, 2, 3], [4, 5, 6]]

deep_copied_list = copy.deepcopy(original_list)

# Modifying the deep_copied_list won't affect the original_list.

deep_copied_list[0][0] = 100

print(original_list)  # Output: [[1, 2, 3], [4, 5, 6]]

In summary, the main difference between deep copy and shallow copy lies in their behavior with nested objects. A shallow copy shares references to nested objects, while a deep copy creates completely independent copies of the entire object hierarchy, ensuring that changes in one do not affect the other. The choice between them depends on your specific use case and whether you want shared or independent copies of nested objects. 

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Python Coding challenge - Day 34 | What is the output of the following Python code?

 

1.0 / 3: This part of the code calculates the division of 1.0 by 3, which is equal to approximately 0.3333333333333333.

'{}' is a string containing a placeholder enclosed in curly braces {}. In this case, {0:.2f} is the placeholder. The 0 inside the curly braces is used to specify the index of the argument to be inserted into the placeholder, and .2f is a format specifier.

:.2f is the format specifier inside the placeholder. Here's what it means:

: separates the index (in this case, 0) from the format specifier.

.2 specifies that you want to display the floating-point number with exactly two decimal places.

f indicates that you want to format the number as a floating-point number.

.format(...): This is the .format() method of the string. It's used to replace the placeholder {0:.2f} with the value of 1.0 / 3 formatted according to the specified format.

When you run this code, the 1.0 / 3 is calculated, resulting in the floating-point number 0.3333333333333333. Then, the .format() method takes this value and formats it to display only two decimal places, resulting in "0.33". Finally, the print() function displays "0.33" to the console.

So, the output of this code is: 0.33 

It formats the result of the division operation to have exactly two decimal places and prints it.

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Python Coding challenge - Day 33 | What is the output of the following Python code?

 

def myfunc(a):

    a = a + 2  # Step 1: Add 2 to the value of 'a' and store it back in 'a'

    a = a * 2  # Step 2: Multiply the updated 'a' by 2 and store it back in 'a'

    return a   # Step 3: Return the final value of 'a'

result = myfunc(2)  # Calling the 'myfunc' function with the argument 2

print(result)       # Printing the result

Here's a detailed explanation of each step:

def myfunc(a):: This line defines a function named myfunc that takes a single argument a.

a = a + 2: Inside the function, this line adds 2 to the value of a. In the context of the function call myfunc(2), a initially has a value of 2, so this line updates a to 4.

a = a * 2: Next, this line multiplies the updated value of a by 2. Since a was updated to 4 in the previous step, this line further updates a to 8.

return a: Finally, the function returns the value of a, which is now 8.

result = myfunc(2): Outside the function, we call myfunc(2) and store the result (which is 8) in a variable named result.

print(result): Finally, we print the value of result, which is 8, to the console.

So, when you run this code, it will output 8 because myfunc(2) performs a series of operations on the input value 2 and returns the result, which is 8.

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Python Coding challenge - Day 32 | What is the output of the following Python code?

 

i = 0  # Initialize the variable i to 0

while i < 3:  # Start a while loop that continues as long as i is less than 3

    print(i)  # Print the current value of i

    i += 1  # Increment the value of i by 1

    print(i + 1)  # Print the value of i + 1 after the increment

Let's break it down step by step:

i = 0: We initialize a variable i and set its initial value to 0.

while i < 3:: This line starts a while loop. The loop will continue executing as long as the value of i is less than 3. In other words, the loop will run when i is 0, 1, or 2, and it will exit when i becomes 3 or greater.

print(i): Inside the loop, we print the current value of i. The first time through the loop, this will print 0.

i += 1: After printing the value of i, we increment its value by 1. So, i changes from 0 to 1.

print(i + 1): We then print the value of i + 1. Since i is now 1, this line will print 2.

The loop returns to the top, and the process repeats if i is still less than 3. In this case, i is still less than 3 (i.e., 1 is less than 3), so the loop continues.

The second iteration of the loop starts with i equal to 1.

print(i): We print the current value of i, which is 1.

i += 1: We increment i by 1, making it equal to 2.

print(i + 1): We print i + 1, which is now 3.

The loop continues for one more iteration with i equal to 2.

print(i): We print 2.

i += 1: We increment i to 3.

print(i + 1): We print i + 1, which is 4.

The loop condition i < 3 is no longer satisfied because i is now equal to 3. Therefore, the loop exits.

So, the output of this code will be as follows: 0 2 1 3 2 4

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Python Coding challenge - Day 31 | What is the output of the following Python code?

 

In the above code a lambda function z that takes an argument x and multiplies it by the value of y, which is set to 8. Then, you call this lambda function with the argument 6 and print the result.

Here's a step-by-step breakdown of what happens:

y = 8: You define a variable y and assign it the value 8.

z = lambda x: x * y: You define a lambda function z that takes an argument x and multiplies it by the value of y. Essentially, it's equivalent to the following regular function definition:

def z(x):

    return x * y

print(z(6)): You call the lambda function z with the argument 6, and it performs the multiplication: 6 * 8, resulting in 48. Then, you print the result, so the output will be: 48

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Python Coding challenge - Day 30 | What is the output of the following Python code?

 

In this line of code, you create a Python list called L containing four elements: 'a', 'b', 'c', and 'd'. This list is stored in memory.

Here's what this line of code does:

" ": This part " " is an empty string. It's used as a separator between the elements of the list when joining them together. In this case, it's an empty string, so there will be no space or any other character between the joined elements.

.join(L): This is a method call on the empty string " ". The join() method is used to concatenate the elements of a list into a single string, with the string used as a separator. In this case, the elements of the list L ('a', 'b', 'c', 'd') will be joined together with an empty string as the separator.

print(...): Finally, the print() function is used to display the result of the join operation. The result, which is the concatenation of the elements in L without any separators, is printed to the console.

So, when you run this code, you'll see the following output: abcd

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Mastering Python Data Structures: From Basics to Advanced

Table of Contents

Chapter 1: Introduction to Data Structures

Chapter 2: Variables and Built-in Types

Chapter 3: Lists and Tuples in Depth

Chapter 4: Sets and Dictionaries

Chapter 5: Strings and Regular Expressions

Chapter 6: Numerical Computing with NumPy

Chapter 7: Stacks and Queues

Chapter 8: Linked Lists

Chapter 9: Trees and Graphs

Chapter 10: Searching and Sorting Algorithms

Chapter 11: Dynamic Programming

Chapter 12: Graph Algorithms

Chapter 13: Algorithmic Techniques

Chapter 14: Advanced Data Structures

Chapter 15: Advanced Algorithms

Chapter 16: Computational Complexity

Chapter 17: Advanced Python Topics

Chapter 18: Python in Web Development

Download - https://clcoding.gumroad.com/l/wgxxnl

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Google Advanced Data Analytics Professional Certificate

 

What you'll learn

Explore the roles of data professionals within an organization 

Create data visualizations and apply statistical methods to investigate data

Build regression and machine learning models to analyze and interpret data

Communicate insights from data analysis to stakeholders

1. Foundations of Data Science
2. Get Started with Python
3. Go Beyond the Numbers: Translate Data into Insights
4. The Power of Statistics
5. Regression Analysis: Simplify Complex Data Relationships
6. The Nuts and Bolts of Machine Learning
7. Google Advanced Data Analytics Capstone

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Google IT Automation with Python Professional Certificate

What you'll learn

Automate tasks by writing Python scripts

Use Git and GitHub for version control

Manage IT resources at scale, both for physical machines and virtual machines in the cloud 

Analyze real-world IT problems and implement the appropriate strategies to solve those problems

Professional Certificate - 6 course series

This beginner-level, six-course certificate, developed by Google, is designed to provide IT professionals with in-demand skills -- including Python, Git, and IT automation -- that can help you advance your career.
Knowing how to write code to solve problems and automate solutions is a crucial skill for anybody in IT.
This program builds on your IT foundations to help you take your career to the next level. It’s designed to teach you how to program with Python and how to use Python to automate common system administration tasks. You'll also learn to use Git and GitHub, troubleshoot and debug complex problems, and apply automation at scale by using configuration management and the Cloud.
This certificate can be completed in about 6 months and is designed to prepare you for a variety of roles in IT, like more advanced IT Support Specialist or Junior Systems Administrator positions. 
We recommend that you have Python installed on your machine. For some courses, you’ll need a computer where you can install Git or ask your administrator to install it for you.

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Python Coding challenge - Day 29 | What is the output of the following Python code?

 

The above code uses the pop() method on a list. Let's break it down step by step:

You have a list named cl with the following elements: [2, 3, 1].

You call the pop(2) method on the list cl. The pop() method in Python is used to remove and return an element from a list at a specified index. In this case, you're specifying index 2, which corresponds to the third element in the list (Python uses zero-based indexing).

The pop(2) method removes the element at index 2, which is the number 1, from the list cl.

The pop(2) method also returns the value that was removed, which is the number 1.

The print() function is used to display the value returned by cl.pop(2). So, it will print 1.

After executing this code, the list cl will be modified to [2, 3], and the number 1 will be printed to the console.

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Understanding Machine Learning with Python 3

 

Use your data to predict future events with the help of machine learning. This course will walk you through creating a machine learning prediction solution and will introduce Python, the scikit-learn library, and the Jupyter Notebook environment.

What you'll learn

Hello! My name is Jerry Kurata, and welcome to Understanding Machine Learning with Python. In this course, you will gain an understanding of how to use Python for Machine Learning. You will get there by covering major topics like:

How to format your problem to be solvable

How to prepare your data for use in a prediction

How to combine that data with algorithms to create models that can predict the future

By the end of this course, you will be able to use Python and the scikit-learn library to create Machine Learning solutions. And you will understand how to evaluate and improve the performance of the solutions you create.

Before you begin, make sure you are already familiar with software development and basic statistics. However, your software experience does not have to be in Python, since you will learn the basics in this course.

When you use Python together with scikit-learn, you will see why this is the preferred development environment for many Machine Learning practitioners. You will do all the demos using the Jupyter Notebook environment. This environment combines live code with narrative text to create a document with can be executed and presented as a web page.

I hope you’ll join me, and I look forward to helping you on your learning journey here at Pluralsight.

JOIN - Understanding Machine Learning with Python 3

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Data Science Challenge (Free Course)

Data Science Challenge 

Duration - Less than 2 hours

Cost - Free

This project requires you to independently complete the following steps:

1.  Importing and preprocessing data

2. Analyze the data

3. Build machine learning models

4. Evaluate machine learning models

Join now - Data Science Challenge (Free Course)

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Python Functions, Files, and Dictionaries (Free Course)

What you'll learn

• Explore the dictionary data structure and user-defined functions in Python.
• Understand concepts like local and global variables, parameter-passing techniques, named functions, and lambda expressions.
• Apply Python's sorted function and control sorting order with custom functions.
• Create a final project involving social media data analysis and CSV file manipulation.

Build your subject-matter expertise

• This course is part of the Python 3 Programming Specialization
• When you enroll in this course, you'll also be enrolled in this Specialization.
• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

Join  - Python Functions, Files, and Dictionaries

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Python Coding challenge - Day 28 | What is the output of the following Python code?

 

In the above code a list a initially defined as [2, 5, 3, 4]. Then, you are trying to insert the value 2 into the list at index 2:2, which is essentially inserting it at position 2 without replacing any existing elements. Here's what happens step by step:

a is initially defined as [2, 5, 3, 4].

a[2:2] = [2] inserts the value 2 into the list a at position 2 without replacing any existing elements.

When you print a, it will display the modified list.

So, when you print a after this operation, you will get the following output: [2, 5, 2, 3, 4]

The value 2 has been inserted at index 2, and the elements after index 2 have been shifted to accommodate the new value.

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IBM: Python Basics for Data Science (Free Course)

 

This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you'll be ready to create your first Python scripts on your own!

About this course

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

What you'll learn

The objectives of this course is to get you started with Python as the programming language and give you a taste of how to start working with data in Python.

In this course you will learn about:

• What Python is and why it is useful
• The application of Python to Data Science
• How to define variables in Python
• Sets and conditional statements in Python
• The purpose of having functions in Python
• How to operate on files to read and write data in Python
• How to use pandas, a must have package for anyone attempting data analysis in Python.

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Harvard University: CS50's Introduction to Cybersecurity (Free Course)

 

About this course

This is CS50's introduction to cybersecurity for technical and non-technical audiences alike. Learn how to protect your own data, devices, and systems from today's threats and how to recognize and evaluate tomorrow's as well, both at home and at work. Learn to view cybersecurity not in absolute terms but relative, a function of risks and rewards (for an adversary) and costs and benefits (for you). Learn to recognize cybersecurity as a trade-off with usability itself. Course presents both high-level and low-level examples of threats, providing students with all they need know technically to understand both. Assignments inspired by real-world events.

What you'll learn

• hacking, cracking
• social engineering, phishing attacks
• passcodes, passwords, SSO
• brute-force attacks, dictionary attacks
• biometrics
• multi-factor authentication, password managers
• ethical hacking
• (distributed) denial-of-service attacks
• viruses, worms, botnets
• SQL injection attacks
• port-scanning
• proxies, firewalls
• automatic updates
• closed-source, open-source software
• buffer-overflow attacks
• secure deletion
• hashing, salting
• secret-key, public-key encryption, digital signatures
• full-disk encryption, ransomware
• cookies, sessions, incognito mode
• anonymization, de-identification
• verification
• operating systems, app stores

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Data Processing Using Python (Free Course)

 



Welcome to learn Data Processing Using Python!

Module 2. Basics of Python

Module 3. Data Acquisition and Presentation

Module 4. Powerful Data Structures and Python Extension Libraries

Module 5. Python Data Statistics and Mining

Module 6.  Object Orientation and Graphical User Interface

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Introduction to Generative AI (Free Course)

 

What you'll learn

• Define Generative AI
• Explain how Generative AI works
• Describe Generative AI Model Types
• Describe Generative AI Applications

This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.

JOIN - Introduction to Generative AI

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Python Coding challenge - Day 27 What is the output of the following Python code?

 

Solutions - 

The above code will extend the list x with individual characters from the string '234', resulting in the list x containing each character as a separate element. Here's the code execution step by step:

x = ['1']: Initializes the list x with one element, which is the string '1'.

x.extend('234'): Extends the list x with the characters from the string '234'. After this line of code, the list x will contain the following elements: ['1', '2', '3', '4'].

print(x): Prints the contents of the list x, which will output: ['1', '2', '3', '4']

So, the final result is a list containing the string '1' and the characters '2', '3', and '4' as separate elements.

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Python Coding challenge - Day 26 What is the output of the following Python code?

 

Solutions - 

The above code uses Python's slice notation to extract a portion of the string s using the slice object x. Here's how it works: 

• s = 'clcoding': This line initializes a string variable s with the value 'clcoding'.
• x = slice(1, 4): This line creates a slice object x that specifies a slice from index 1 (inclusive) to index 4 (exclusive). In other words, it selects the characters at positions 1, 2, and 3 in the string s.
• print(s[x]): This line uses the slice object x to extract the characters from the string s according to the specified slice. The characters at positions 1, 2, and 3 in the string 'clcoding' are 'lco', and these characters are printed to the console.

So, when you run the code, it will output: lco

The slice s[x] extracts the characters from index 1 to 3 (4 is exclusive) in the string 'clcoding', which are 'lco'.

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Learn Python Quickly: A Complete Beginner’s Guide to Learning Python, Even If You’re New to Programming: Crash Course with Hands-On Project, Book

FREE Book 📙 

 Looking to learn Python?

Python has gone to be one of the most popular programming languages in the world, and you will be one of the few people left out if you don’t add this knowledge to your arsenal. If you’re looking to learn Python, now is an excellent time to do so. But where do you begin?

You can start right here, right now, with this audiobook. It makes learning Python simple, fast, and easy, taking away the confusion from learning a new language. When learning a new language, it's easy to be overwhelmed and not know where to start or what to focus on. You can spend a long time pursuing tutorials online only to find out you don't really understand any of the concepts they covered. That won't be a problem here! This audiobook follows a step-by-step guide, walking you through everything you need to know about Python in an easy to follow fashion. It will teach you all the basics of Python, and even some of the more advanced Python concepts, taking you from beginner to intermediate Python programmer.

This audiobook will give you:

• A solid foundation in Python programming.
• Intermediate and advanced topics once you’ve mastered the basics.
• Simple explanations of code, broken down into easy to follow steps.
• Python programming exercises and solutions.
• Two projects at the end of the audiobook designed to help you bring all the concepts you’ve learned together.
• Source code files you can refer to and run on your computer.

The exercises in this audiobook are designed to help you practice using the skills you’ve learned in the various sections. The final two projects will let you practice putting everything you’ve learned together and teaching you how to manipulate text, work with images, and create a simple Graphical User Interface (GUI).

Link - Learn Python Quickly: A Complete Beginner’s Guide to Learning Python, Even If You’re New to Programming: Crash Course with Hands-On Project, Book

This audiobook will help you master the following topics:

• Working with Python in both the command line and an Integrated Development Environment (IDE)
• Variables and operators
• Python data types
• Python data structures
• Handling inputs and outputs
• Getting user inputs
• Conditional/control flow statements
• Error handling
• Functions, parameters, and scope
• Built-in function
• Creating modules
• Object-oriented programming
• Reading and writing files
• Recursion
• Image handling

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Python Coding challenge - Day 25 What is the output of the following Python code?

 

The above code will split the string r into a list using the default whitespace separator, but since there are no whitespace characters in the string '123', it will not split the string, and you will get a list with the original string as its only element. Here's the output you would get: ['123']

The split() method without any arguments splits a string by whitespace characters (spaces, tabs, newlines, etc.). Since '123' contains no whitespace characters, it remains as a single element in the list. If you want to split '123' into individual digits, you can use an empty string as the argument to the split() method like this:

r = '123'

print(r.split(''))

However, this will raise an error because an empty string cannot be used as a separator. If you want to split '123' into individual characters as strings, you can do it like this:

r = '123'

split_list = list(r)

print(split_list)

This will output:

['1', '2', '3']

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Regular Expressions in Python

 

What you'll learn

Construct regex patterns

Validate passwords and user input in web forms

Extract patterns and replace strings with regex

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

Introduction to Regular Expressions in Python

Intermediate Regular Expressions in Python

Password Validation with Regular Expressions

Form and User Input Validation with Regular Expressions

Extraction and Word Replacement from Server Logs 

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Clean and analyze social media usage data with Python

 

Objectives

Increase client reach and engagement

Gain valuable insights that will help improve social media performance

Achieve their social media goals and provide data-driven recommendations

Project plan

This project requires you to independently complete the following steps:

• Import required libraries
• Generate random data for the social media data
• Load the data into a Pandas DataFrame and explore the data
• Clean the data
• Visualize and analyze the data

Join - Clean and analyze social media usage data with Python

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Python Coding challenge - Day 24 What is the output of the following Python code?

 

The above code provided is creating two sets, st1 and st2, and then using the - operator to find the difference between st2 and st1. The - operator in this context performs a set difference operation, which returns a new set containing the elements that are in st2 but not in st1.

Here's the step-by-step breakdown of the code:

st1 is a set containing the elements {1, 2, 3}.

st2 is a set containing the elements {2, 3, 4}.

st2 - st1 calculates the set difference between st2 and st1.

The result of st2 - st1 will be a new set containing the elements that are in st2 but not in st1. In this case, it will be {4}, because 4 is in st2 but not in st1.

So, when you run the code, it will output: 4

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Free python programming books

 

There are many free Python programming books available online that can help you learn Python or improve your Python skills. Here are some notable ones:

Download - Free Python Programming books 

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Free Python and Statistics for Financial Analysis

 

There are 4 modules in this course

Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry.  The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data.

By the end of the course, you can achieve the following using python:

- Import, pre-process, save and visualize financial data into pandas Dataframe

- Manipulate the existing financial data by generating new variables using multiple columns

- Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts

- Build a trading model using multiple linear regression model 

- Evaluate the performance of the trading model using different investment indicators

Jupyter Notebook environment is configured in the course platform for practicing python coding without installing any client applications.

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Foundations of Data Science: K-Means Clustering in Python (Free Course)

 

What you'll learn

Define and explain the key concepts of data clustering    

Demonstrate understanding of the key constructs and features of the Python language.    

Implement in Python the principle steps of the K-means algorithm.    

Design and execute a whole data clustering workflow and interpret the outputs.    

Free Join - Foundations of Data Science: K-Means Clustering in Python

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Problem: Implement a Stack using Python

 Implement a stack data structure in Python. A stack is a linear data structure that follows the Last-In, First-Out (LIFO) principle, where the last element added to the stack is the first one to be removed.

Your task is to create a Python class called Stack that has the following methods:

push(item): Adds an item to the top of the stack.

pop(): Removes and returns the item from the top of the stack.

peek(): Returns the item currently at the top of the stack without removing it.

is_empty(): Returns True if the stack is empty, and False otherwise.

size(): Returns the number of items in the stack.

You can implement the stack using a list as the underlying data structure.

Here's a basic structure for the Stack class:

class Stack:

    def __init__(self):

        # Initialize an empty stack

        pass

    def push(self, item):

        # Add item to the top of the stack

        pass

    def pop(self):

        # Remove and return the item from the top of the stack

        pass

    def peek(self):

        # Return the item at the top of the stack without removing it

        pass

    def is_empty(self):

        # Return True if the stack is empty, False otherwise

        pass

    def size(self):

        # Return the number of items in the stack

        pass

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What is the purpose of the @property decorator in Python?

A) It marks a method as a property, allowing it to be accessed like an attribute.

B) It defines a new class.

C) It marks a method as static, meaning it can only be called on the class and not on instances of the class.

D) It marks a method as a class method.

Answer:

A) It marks a method as a property, allowing it to be accessed like an attribute.

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What is the purpose of the __str__ method in a Python class?

A) It defines a new instance variable.

B) It initializes the class object.

C) It specifies the return type of a method.

D) It defines a string representation of the object when using `str()`.

Answer : 

D) It defines a string representation of the object when using str().

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10 New AI tools you will regret not knowing:

 10 New AI tools you will regret not knowing: 

1. 10web.io: An AI-powered website builder that likely simplifies the process of creating websites using artificial intelligence.

2. Docus.ai: An AI health assistant, which could potentially help with healthcare-related tasks such as patient data analysis or medical research.

3. Postwise.ai: An AI tool for content creation, which can be useful for generating written content efficiently.

4. Stockimg.ai: An AI tool for creating logos and images, possibly using AI to generate or enhance visual content.

5. Tabnine.com: A coding assistant powered by AI, which can assist developers in writing code more efficiently and effectively.

6. Longshot.ai: Potentially an AI tool for generating blog posts or other written content.

7. Voicemaker.in: An AI tool that might help with generating artificial voices or assisting with voice-related tasks.

8. Franks.ai: An AI search engine, which could provide advanced search capabilities using artificial intelligence algorithms.

9. Gling.ai: An AI video editor, likely designed to simplify the process of editing and enhancing videos.

10. Perplexity.ai: This is related to research

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List of top 10 data science books using Python in 2023

 

1. "Python for Data Analysis" by Wes McKinney - This book focuses on data manipulation and analysis using Python's pandas library.

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process. 

Download -  Python for Data Analysis

2. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron - A practical guide to machine learning using Python libraries like Scikit-Learn, Keras, and TensorFlow.

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.

Use Scikit-learn to track an example ML project end to end

Explore several models, including support vector machines, decision trees, random forests, and ensemble methods

Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection

Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers

Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

Download - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

3.  "Data Science from Scratch" by Joel Grus - A beginner-friendly introduction to data science concepts and tools using Python.

To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.

Get a crash course in Python

Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science

Collect, explore, clean, munge, and manipulate data

Dive into the fundamentals of machine learning

Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering

Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Download - Data Science from Scratch: First Principles with Python

4. "Python Data Science Handbook" by Jake VanderPlas - Covers essential data science libraries in Python, such as NumPy, pandas, Matplotlib, and Scikit-Learn.

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you'll learn how:

IPython and Jupyter provide computational environments for scientists using Python

NumPy includes the ndarray for efficient storage and manipulation of dense data arrays

Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data

Matplotlib includes capabilities for a flexible range of data visualizations

Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms

Download  -  Python Data Science Handbook

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - A comprehensive reference on deep learning techniques and applications.

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. 

Download        -  Deep Learning (Adaptive Computation and Machine Learning series)

"Data Science for Business" by Foster Provost and Tom Fawcett - Explores the intersection of data science and business decision-making. 

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

Understand how data science fits in your organization—and how you can use it for competitive advantage

Treat data as a business asset that requires careful investment if you’re to gain real value

Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way

Learn general concepts for actually extracting knowledge from data

Apply data science principles when interviewing data science job candidates

Download - Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili - A hands-on guide to machine learning with Python and its libraries.

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. 

Download   -     Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

"Practical Statistics for Data Scientists" by Andrew Bruce and Peter Bruce - Provides a practical understanding of statistical concepts for data analysis.

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:

Why exploratory data analysis is a key preliminary step in data science

How random sampling can reduce bias and yield a higher-quality dataset, even with big data

How the principles of experimental design yield definitive answers to questions

How to use regression to estimate outcomes and detect anomalies

Key classification techniques for predicting which categories a record belongs to

Statistical machine learning methods that "learn" from data

Unsupervised learning methods for extracting meaning from unlabeled data

Download - Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

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100 Python Interview questions

1.  Python Program to Print Hello world!
2. Python Program to Add Two Numbers
3. Python Program to Find the Square Root
4. Python Program to Calculate the Area of a Triangle
5. Python Program to Solve Quadratic Equation
6. Python Program to Swap Two Variables
7. Python Program to Generate a Random Number
8. Python Program to Convert Kilometers to Miles
9. Python Program to Convert Celsius To Fahrenheit
10. Python Program to Check if a Number is Positive, Negative or 0
11. Python Program to Check if a Number is Odd or Even
12. Python Program to Check Leap Year
13. Python Program to Find the Largest Among Three Numbers
14. Python Program to Check Prime Number
15. Python Program to Print all Prime Numbers in an Interval
16. Python Program to Find the Factorial of a Number
17. Python Program to Display the multiplication Table
18. Python Program to Print the Fibonacci sequence
19. Python Program to Check Armstrong Number
20. Python Program to Find Armstrong Number in an Interval
21. Python Program to Find the Sum of Natural Numbers
22. Python Program to Display Powers of 2 Using Anonymous Function
23. Python Program to Find Numbers Divisible by Another Number
24. Python Program to Convert Decimal to Binary, Octal and Hexadecimal
25. Python Program to Find ASCII Value of Character
26. Python Program to Find HCF or GCD
27. Python Program to Find LCM
28. Python Program to Find the Factors of a Number
29. Python Program to Make a Simple Calculator
30. Python Program to Shuffle Deck of Cards
31. Python Program to Display Calendar
32. Python Program to Display Fibonacci Sequence Using Recursion
33. Python Program to Find Sum of Natural Numbers Using Recursion
34. Python Program to Find Factorial of Number Using Recursion
35. Python Program to Convert Decimal to Binary Using Recursion
36. Python Program to Add Two Matrices
37. Python Program to Transpose a Matrix
38. Python Program to Multiply Two Matrices
39. Python Program to Check Whether a String is Palindrome or Not
40. Python Program to Remove Punctuations From a String
41. Python Program to Sort Words in Alphabetic Order
42. Python Program to Illustrate Different Set Operations
43. Python Program to Count the Number of Each Vowel
44. Python Program to Merge Mails
45. Python Program to Find the Size (Resolution) of a Image
46. Python Program to Find Hash of File
47. Python Program to Create Pyramid Patterns
48. Python Program to Merge Two Dictionaries
49. Python Program to Safely Create a Nested Directory
50. Python Program to Access Index of a List Using for Loop
51. Python Program to Flatten a Nested List
52. Python Program to Slice Lists
53. Python Program to Iterate Over Dictionaries Using for Loop
54. Python Program to Sort a Dictionary by Value
55. Python Program to Check If a List is Empty
56. Python Program to Catch Multiple Exceptions in One Line
57. Python Program to Copy a File
58. Python Program to Concatenate Two Lists
59. Python Program to Check if a Key is Already Present in a Dictionary
60. Python Program to Split a List Into Evenly Sized Chunks
61. Python Program to Parse a String to a Float or Int
62. Python Program to Print Colored Text to the Terminal
63. Python Program to Convert String to Datetime
64. Python Program to Get the Last Element of the List
65. Python Program to Get a Substring of a String
66. Python Program to Print Output Without a Newline
67. Python Program Read a File Line by Line Into a List
68. Python Program to Randomly Select an Element From the List
69. Python Program to Check If a String Is a Number (Float)
70. Python Program to Count the Occurrence of an Item in a List
71. Python Program to Append to a File
72. Python Program to Delete an Element From a Dictionary
73. Python Program to Create a Long Multiline String
74. Python Program to Extract Extension From the File Name
75. Python Program to Measure the Elapsed Time in Python
76. Python Program to Get the Class Name of an Instance
77. Python Program to Convert Two Lists Into a Dictionary
78. Python Program to Differentiate Between type() and isinstance()
79. Python Program to Trim Whitespace From a String
80. Python Program to Get the File Name From the File Path
81. Python Program to Represent enum
82. Python Program to Return Multiple Values From a Function
83. Python Program to Get Line Count of a File
84. Python Program to Find All File with .txt Extension Present Inside a Directory
85. Python Program to Get File Creation and Modification Date
86. Python Program to Get the Full Path of the Current Working Directory
87. Python Program to Iterate Through Two Lists in Parallel
88. Python Program to Check the File Size
89. Python Program to Reverse a Number
90. Python Program to Compute the Power of a Number
91. Python Program to Count the Number of Digits Present In a Number
92. Python Program to Check If Two Strings are Anagram
93. Python Program to Capitalize the First Character of a String
94. Python Program to Compute all the Permutation of the String
95. Python Program to Create a Countdown Timer
96. Python Program to Count the Number of Occurrence of a Character in String
97. Python Program to Remove Duplicate Element From a List
98. Python Program to Convert Bytes to a String

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Eid-ul-Adha Mubarak wish using Python

 

Code : 

import pyfiglet

import random

font = random.choice(pyfiglet.FigletFont.getFonts())

ascii_art = pyfiglet.figlet_format("Eid-ul-Adha", font=font)

greeting = f"{ascii_art}\nEid-ul-Adha Mubarak!\n{ascii_art}"

print(greeting)

#clcoding.com

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Python libraries commonly used in oceanographic research

  

Python libraries commonly used in oceanographic research:

NumPy and SciPy: These libraries provide powerful numerical and scientific computing capabilities, including array manipulation, linear algebra, optimization, and signal processing.

Pandas: Pandas is a library used for data manipulation and analysis. It provides data structures and functions for efficient handling and processing of structured data, such as time series or oceanographic datasets.

Matplotlib and Seaborn: These libraries are used for data visualization in Python. Matplotlib provides a wide range of plotting functions, while Seaborn offers a high-level interface for creating attractive statistical graphics.

Cartopy: Cartopy is a library for geospatial data processing and mapping. It allows you to create maps, plot geographical data, and perform geospatial transformations.

Xarray and NetCDF4: These libraries are commonly used for handling and analyzing multidimensional gridded data, such as ocean model outputs or satellite observations. They provide efficient I/O operations, metadata handling, and mathematical operations on multidimensional arrays.

Ocean Data View (ODV): ODV is a popular software tool for oceanographic data visualization and analysis. While not a Python library, it can be integrated with Python using the PyODV package, allowing you to import, analyze, and plot ODV data files.

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Free Python Notes for Interview

 

https://clcoding.gumroad.com/l/gwaaj

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10 Python terms that beginners tend to confuse

 

Variable vs. Value: Beginners often confuse variables and values in Python. A variable is a name used to store a value, while a value is the actual data stored in the variable. For example, in the statement x = 5, x is the variable, and 5 is the value assigned to it.

List vs. Tuple: Beginners may struggle with understanding the differences between lists and tuples in Python. A list is a mutable sequence of elements enclosed in square brackets ([]), while a tuple is an immutable sequence enclosed in parentheses (()). This means that you can modify a list by adding, removing, or changing elements, but you cannot do the same with a tuple once it is created.

Function vs. Method: Beginners sometimes confuse functions and methods. A function is a block of reusable code that performs a specific task, while a method is a function that belongs to an object and is called using the dot notation (object.method()). Functions can be called independently, whereas methods are invoked on specific objects.

Syntax Error vs. Runtime Error: Beginners often mix up syntax errors and runtime errors. A syntax error occurs when the code violates the language's grammar rules and prevents it from being compiled or interpreted correctly. On the other hand, a runtime error occurs when the code is syntactically correct, but an error is encountered while the program is running.

Index vs. Slice: Understanding the difference between indexing and slicing can be confusing for beginners. Indexing refers to accessing a specific element in a sequence, such as a string or a list, by specifying its position using square brackets ([]). Slicing, on the other hand, allows you to extract a portion of a sequence by specifying a range of indices using the colon (:) notation.

Mutable vs. Immutable: Beginners may struggle with grasping the concept of mutable and immutable objects in Python. Mutable objects can be modified after they are created, while immutable objects cannot. For example, lists are mutable, so you can change their elements, whereas strings are immutable, so you cannot modify their characters once they are created.

Importing Modules vs. Installing Packages: Beginners sometimes confuse importing modules and installing packages. Importing a module allows you to use its predefined functions, classes, or variables in your code by using the import statement. On the other hand, installing a package refers to downloading and setting up additional libraries or modules that are not included in the Python standard library, usually using package managers like pip.

Syntax vs. Semantics: Beginners may have difficulty understanding the distinction between syntax and semantics. Syntax refers to the rules and structure of a programming language, including the correct placement of punctuation, keywords, and symbols. Semantics, on the other hand, relates to the meaning and interpretation of the code. Syntax errors occur when the code violates the language's syntax rules, while semantic errors occur when the code produces unexpected or incorrect results due to logical or conceptual mistakes.

Class vs. Object: Beginners often struggle with the concepts of classes and objects in object-oriented programming. A class is a blueprint or template that defines the structure and behavior of objects, while an object is an instance of a class. In simpler terms, a class can be thought of as a blueprint for creating multiple objects with similar characteristics and behaviors.

Global vs. Local Variables: Understanding the scope of variables can be confusing for beginners. Global variables are defined outside of any function or class and can be accessed from any part of the program. Local variables, on the other hand, are defined within a function or a block of code and can only be accessed within that specific function or block. Beginners may encounter issues when they unintentionally create variables with the same name in different scopes, leading to unexpected behavior or errors.

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Data Analytics Course Handwritten Notes

Introduction:

In today's digital age, where typing on keyboards and tapping on screens has become the norm, there is something truly magical about the simplicity and authenticity of handwritten notes. Handwritten notes have a unique charm that digital text cannot replicate. They are a reflection of our personality, creativity, and individuality. In this blog, I want to share my newfound love for handwritten notes and the joy they bring.

The Art of Handwriting:

Handwriting is an art form that allows us to express ourselves in a personal and intimate way. Each stroke of the pen carries a piece of our emotions, thoughts, and ideas. Whether it's elegant cursive, playful doodles, or colorful illustrations, our handwriting reveals a glimpse of our character. Handwritten notes offer a tangible connection between the writer and the reader, creating a more intimate and meaningful experience.

Unleashing Creativity:

Writing by hand stimulates our creativity and imagination. As we put pen to paper, ideas flow more freely, and we are more likely to explore new perspectives and insights. The act of writing itself becomes a therapeutic process, allowing us to slow down, focus, and fully engage with our thoughts. Handwritten notes offer a canvas for our creativity to flourish, enabling us to experiment with different styles, fonts, and embellishments.

A Personal Touch:

When we receive a handwritten note, it feels like a precious gift. The time and effort invested in crafting the note make it a unique and personal gesture. Whether it's a heartfelt letter, a thoughtful thank-you card, or a quick reminder, handwritten notes show that we care. They create a deeper connection and leave a lasting impression on the recipient, unlike impersonal digital messages that can easily be forgotten.

Preserving Memories:

Handwritten notes have an enduring quality that transcends time. They become treasured keepsakes, reminding us of special moments, important milestones, and cherished relationships. Stumbling upon a box of old handwritten letters can evoke a wave of nostalgia and bring back vivid memories. In a world where digital files can be lost or corrupted, handwritten notes stand as tangible and irreplaceable mementos of our lives.

Sharing Handwritten Notes:

In the spirit of celebrating the beauty of handwritten notes, I am excited to share my own collection of handwritten notes with you all. Through my blog and social media platforms, I will be posting images and stories behind my notes, discussing different techniques and styles, and even providing tips on improving handwriting skills. I hope to inspire others to rediscover the joy of writing by hand and to embrace the personal touch that handwritten notes bring to our lives.

Click Here for notes

Conclusion:

Handwritten notes are not merely pieces of paper; they are vessels of our thoughts, emotions, and creativity. They allow us to connect on a deeper level, create lasting memories, and express ourselves in a way that digital text cannot replicate. So, let's bring back the beauty of handwritten notes, one stroke of the pen at a time, and embrace the power of personal expression. Together, let's make the world a little brighter with our handwritten notes.

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