Learning Python: Learn to code like a professional with Python - an open source, versatile, and powerful programming language

Learn to code like a professional with Python - an open source, versatile, and powerful programming language

Key Features

• Learn the fundamentals of programming with Python - one of the best languages ever created
• Develop a strong set of programming skills that you will be able to express in any situation, on every platform, thanks to Python's portability
• Create outstanding applications of all kind, from websites to scripting, and from GUIs to data science

Book Description

Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application.

The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned. 

What you will learn

• Get Python up and running on Windows, Mac, and Linux in no time
• Grasp the fundamental concepts of coding, along with the basics of data structures and control flow.
• Write elegant, reusable, and efficient code in any situation
• Understand when to use the functional or the object oriented programming approach
• Create bulletproof, reliable software by writing tests to support your code
• Explore examples of GUIs, scripting, data science and web applications
• Learn to be independent, capable of fetching any resource you need, as well as dig deeper

Who this book is for

Python is the most popular introductory teaching language in U.S. top computer science universities, so if you are new to software development, or maybe you have little experience, and would like to start off on the right foot, then this language and this book are what you need. Its amazing design and portability will help you become productive regardless of the environment you choose to work with.

Table of Contents

1. Introduction and first steps - Take a deep breath
2. Built-in data types
3. Iterating and making decisions
4. Functions, the building blocks of code
5. Saving time and memory
6. Advanced Concepts - OOP, Decorators, Iterators
7. Testing, Profiling and dealing with Exceptions
8. The edges: GUIs and Scripts
9. Data Science
10. Web Development done right
11. Debugging and troubleshooting
12. Summing up: a complete example

BUY - Learning Python: Learn to code like a professional with Python - an open source, versatile, and powerful programming language

Read More

Python Coding challenge - Day 39 | What is the output of the following Python code?

 

The code is a simple Python while loop that starts with the variable cl set to 4. It increments cl by 1 in each iteration and prints the updated value of cl followed by a hyphen ("-") until cl is no longer less than 9. Here's the output of the code: 5-6-7-8-9-

The loop starts with cl equal to 4, and in each iteration, it increments cl by 1. When cl reaches 9, the loop stops because the condition cl < 9 is no longer true.

 step by step solutions 

Initialize the variable cl with the value 4:

cl = 4

Start a while loop with the condition cl < 9. This means the loop will continue as long as cl is less than 9.

Inside the loop, increment the value of cl by 1. This is done using the cl = cl + 1 statement:

cl = cl + 1

Print the updated value of cl, followed by a hyphen (-) without moving to the next line due to the end parameter:

print(cl, end='-')

The loop will continue to the next iteration or exit the loop depending on whether the condition cl < 9 is still true.

The loop repeats steps 3-5 until the condition cl < 9 is no longer true. When cl becomes equal to 9, the loop exits.

Here's the output produced by the code, showing each step:

Step 1: cl = 4

Step 2: Entering the while loop with the condition cl < 9 (4 < 9 is true)

Step 3: Incrementing cl by 1, cl is now 5

Step 4: Printing 5 with an end of '-', output so far: 5-

Step 2: Entering the while loop with the condition cl < 9 (5 < 9 is true)

Step 3: Incrementing cl by 1, cl is now 6

Step 4: Printing 6 with an end of '-', output so far: 5-6-

Step 2: Entering the while loop with the condition cl < 9 (6 < 9 is true)

Step 3: Incrementing cl by 1, cl is now 7

Step 4: Printing 7 with an end of '-', output so far: 5-6-7-

Step 2: Entering the while loop with the condition cl < 9 (7 < 9 is true)

Step 3: Incrementing cl by 1, cl is now 8

Step 4: Printing 8 with an end of '-', output so far: 5-6-7-8-

Step 2: Entering the while loop with the condition cl < 9 (8 < 9 is true)

Step 3: Incrementing cl by 1, cl is now 9

Step 4: Printing 9 with an end of '-', output so far: 5-6-7-8-9-

Step 2: Exiting the while loop as the condition cl < 9 is no longer true

The final output is 5-6-7-8-9-, which is the result of running the code.

Read More

IBM Data Analyst Professional Certificate

 Prepare for a career as a data analyst. Gain the in-demand skills and hands-on experience to get job-ready in as little as 4 months. No prior experience required.

What you'll learn

Master the most up-to-date practical skills and tools that data analysts use in their daily roles

Learn how to visualize data and present findings using various charts in Excel spreadsheets and BI tools like IBM Cognos Analytics & Tableau

Develop working knowledge of Python language for analyzing data using Python libraries like Pandas and Numpy, and invoke APIs and Web Services 

Gain technical experience through hands on labs and projects and build a portfolio to showcase your work

Prepare for a career in Data Analytics

Receive professional-level training from IBM

Demonstrate your proficiency in portfolio-ready projects

Earn an employer-recognized certificate from IBM

Qualify for in-demand job titles: Data Analyst, Associate Data Analyst, Business Analyst

JOIN - IBM Data Analyst Professional Certificate

Read More

Python Coding challenge - Day 38 | What is the output of the following Python code?

 

py = 2 + 3: In this line, you're creating a variable named py and assigning it the result of the addition of 2 and 3, which is 5. So, py now holds the value 5.

print('py'): In this line, you are using the print() function to display the string 'py'. This code will not print the value of the variable py but will directly print the string 'py' as it is enclosed in single quotes.

Here's your code with step-by-step explanations: 

# Step 1: Calculate the sum of 2 and 3

py = 2 + 3  # py is assigned the value 5

# Step 2: Print the string 'py'

print('py')   # This will print the string 'py' to the console

When you run this code, it will print the string 'py' to the console, not the value stored in the variable py.

Read More

Cybersecurity for Everyone (Free Course)

 

There are 6 modules in this course

Cybersecurity affects everyone, including in the delivery of basic products and services. If you or your organization want to better understand how to address your cybersecurity, this is the course for you and your colleagues to take -- from seasoned professionals to your non-technical colleagues.

Your instructor, Dr. Charles Harry, has served on the front lines with the NSA (National Security Agency) and as an expert advising corporate and institutional leaders on managing cybersecurity risk. And he brings a rare and engaging perspective to help you learn cybersecurity from the ground up.

Cybersecurity for Everyone lays the groundwork to understand and explore the key issues facing policy makers attempting to manage the problem of cybersecurity, from its technical foundations to the domestic and international policy considerations surrounding governance, privacy, and risk management, to applications for achieving the goals of an enterprise, an institution, or a nation. This course is designed for students with some or no background in information technology, whether a novice or active in the cybersecurity field (engineers and computer scientists will learn the broader context and business aspects of cybersecurity), and will provide the principles to understand the current debates shaping a rapidly evolving security landscape.

JOIN - Cybersecurity for Everyone

Read More

Python Coding challenge - Day 37 | What is the output of the following Python code?

 

Solutions - 

In the code a function f that takes two parameters, value and values. The function then modifies the value at the first index of the values list to be 44 and assigns 1 to a variable v, but this v variable is not used anywhere in the function.

After defining the function f, you have a variable t set to 3 and a list v with three elements: [1, 2, 3]. You then call the function f with the arguments t and v, and finally, you print the values of t and the first element of v after the function call.

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

t is initially set to 3.

v is initially set to [1, 2, 3].

The function f is called with the arguments t and v.

Inside the function, the value at the first index of the values list (which is the same list as v) is modified to be 44. So, v becomes [44, 2, 3]. However, the variable v declared in the function and set to 1 is not used.

After the function call, you print the values of t and the first element of v, which is v[0].

As a result, when you run the code, it will print: 3 44

Read More

Using Databases with Python

 

What you'll learn

Use the Create, Read, Update, and Delete operations to manage databases

Explain the basics of Object Oriented Python

Understand how data is stored across multiple tables in a database

Utilize the Google Maps API to visualize data

Build your subject-matter expertise

This course is part of the Python for Everybody 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 - Using Databases with Python

Read More

Introduction to Image Generation (Free Course)

 

What you'll learn

How diffusion models work

Real use-cases for diffusion models

Unconditioned diffusion models

Advancements in diffusion models (text-to-image)

There is 1 module in this course

This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI. 

JOIN FREE  - Introduction to Image Generation

Read More

Python Coding challenge - Day 36 | What is the output of the following Python code?

 

Solutions - 

# Define the input string

input_string = 'cd'

# Use the partition method to split the string

result = input_string.partition('cd')

# The 'result' variable now contains a tuple with three elements

# Element 0: The part of the string before the partitioned substring

# Element 1: The partitioned substring itself

# Element 2: The part of the string after the partitioned substring

# Print the result

print(result)

In this case, the input string is 'cd', and the partition method is used to split it into three parts. The print statement will display the following result: ('', 'cd', '')

Here's a breakdown of the result:

Element 0 is an empty string '' because there is nothing before the partitioned substring 'cd' in the input string.

Element 1 is the partitioned substring itself, which is 'cd'.

Element 2 is an empty string '' because there is nothing after the partitioned substring 'cd' in the input string.

This is the expected output when using the partition method with 'cd' as both the input string and the partitioned substring.

Read More

Python Coding challenge - Day 35 | 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 while i is less than 3

    print(i)  # Print the current value of i

    i += 1  # Increment i by 1

# The while loop will run three times:

# 1. i is 0, so it prints 0 and increments i to 1.

# 2. i is 1, so it prints 1 and increments i to 2.

# 3. i is 2, so it prints 2 and increments i to 3.

else:  # After the while loop is finished, execute the else block

    print(0)  # Print 0

# The else block is executed once after the while loop is done.

# The output will be:

# 0

# 1

# 2

# 0Here's the step-by-step explanation of what happens:

Initialize i to 0.

The while loop checks if i is less than 3, which is true since i is 0.

It enters the loop and prints the current value of i, which is 0.

It then increments i by 1, making it 1.

The loop iterates again since i (1) is still less than 3. It prints 1.

i is incremented to 2, and the loop continues. It prints 2.

After the third iteration, i becomes 3, and the condition i < 3 is no longer true.

The loop terminates, and the code in the else block is executed, which prints 0.

Read More

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

JOIN - HarvardX: CS50's Introduction to Programming with Python

Read More

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. 

Read More

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.

Read More

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.

Read More

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

Read More

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

Read More

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

Read More

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

Read More

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

JOIN - Google Advanced Data Analytics Professional Certificate

Read More

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.

JOIN - Google IT Automation with Python Professional Certificate

Read More

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.

Read More

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

Read More

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)

Read More

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

Read More

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.

Read More

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.

JOIN - IBM: Python Basics for Data Science

Read More

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

JOIN - Harvard University: CS50's Introduction to Cybersecurity

Read More

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

Join - Data Processing Using Python

Read More

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

Read More

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.

Read More

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'.

Read More

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

Read More

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']

Read More

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 

JOIN - Regular Expressions in Python

Read More

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

Read More

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

Read More

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 

Read More

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.

JOIN  - Python and Statistics for Financial Analysis

Read More

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

Read More

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

Read More

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.

Read More

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().

Read More

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

Read More

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

Read More