6 Python String Things I Regret Not Knowing Earlier

 

F-Strings for Formatting:

F-strings (formatted string literals), introduced in Python 3.6, are a concise and readable way to embed expressions inside string literals. They make string interpolation much easier.

name = "Alice"

age = 30

print(f"Name: {name}, Age: {age}")

#clcoding.com

Name: Alice, Age: 30

String Methods:

Python’s string methods like strip(), replace(), and split() can save a lot of time and lines of code.

text = " Hello, World! "

print(text.strip())  

print(text.replace("World", "Python")) 

print(text.split(','))  

#clcoding.com

Hello, World!

 Hello, Python! 

[' Hello', ' World! ']

Joining Lists into Strings:

Using the join() method to concatenate a list of strings into a single string is both efficient and convenient.

words = ["Python", "is", "awesome"]

sentence = " ".join(words)

print(sentence)  

#clcoding.com

Python is awesome

Multiline Strings:

Triple quotes (''' or """) allow for easy multiline strings, which can be useful for writing long text or docstrings.

multiline_string = """

This is a

multiline string

in Python.

"""

print(multiline_string)

#clcoding.com

This is a

multiline string

in Python.

String Slicing:

String slicing allows for extracting parts of a string. Understanding how to use slicing can simplify many tasks.

text = "Hello, World!"

print(text[7:12]) 

print(text[::-1])  

#clcoding.com

World

!dlroW ,olleH

Using in for Substring Checks:

Checking if a substring exists within a string using the in keyword is simple and effective.

text = "The quick brown fox"

print("quick" in text)  # True

print("slow" in text)  # False

#clcoding.com

True

False

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Did You Know — Python Has A Built-in Priority Queue

 

Did You Know — Python Has A Built-in Priority Queue

import queue

# Create a priority queue

pq = queue.PriorityQueue()

# Add items to the queue with a priority number 

pq.put((1, 'Task with priority 1'))

pq.put((3, 'Task with priority 3'))

pq.put((2, 'Task with priority 2'))

# Retrieve items from the queue

while not pq.empty():

    priority, task = pq.get()

    print(f'Priority: {priority}, Task: {task}')

    

#clcoding.com

Priority: 1, Task: Task with priority 1

Priority: 2, Task: Task with priority 2

Priority: 3, Task: Task with priority 3

 

Read More

Modern Computer Vision with PyTorch - Second Edition: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

 

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models

Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features

- Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models

- Build solutions for real-world computer vision problems using PyTorch

- All the code files are available on GitHub and can be run on Google Colab

Book Description

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks.

The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion.

You'll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You'll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you'll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you'll learn best practices for deploying a model to production.

By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.

What you will learn

- Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer

- Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks

- Implement multi-object detection and segmentation

- Leverage foundation models to perform object detection and segmentation without any training data points

- Learn best practices for moving a model to production

Who this book is for

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.

Table of Contents

- Artificial Neural Network Fundamentals

- PyTorch Fundamentals

- Building a Deep Neural Network with PyTorch

- Introducing Convolutional Neural Networks

- Transfer Learning for Image Classification

- Practical Aspects of Image Classification

- Basics of Object Detection

- Advanced Object Detection

- Image Segmentation

- Applications of Object Detection and Segmentation

- Autoencoders and Image Manipulation

- Image Generation Using GANs

SOFT Copy: Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Hard Copy: Modern Computer Vision with PyTorch - Second Edition: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI 2nd ed. Edition by V Kishore Ayyadevara (Author), Yeshwanth Reddy (Author)

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Python Cookbook : Everyone can cook delicious recipes 300+

 

Learn to cook delicious and fun recipes in Python. codes that will help you grow in the programming environment using this wonderful language.

Some of the recipes you will create will be related to: Algorithms, classes, flow control, functions, design patterns, regular expressions, working with databases, and many more things.

Learning these recipes will give you a lot of confidence when you are creating great programs and you will have more understanding when reading live code.

Hard Copy: Python Cookbook : Everyone can cook delicious recipes 300+

Soft Copy: Python Cookbook : Everyone can cook delicious recipes 300+

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7 level of writing Python Dictionary

Level 1: Basic Dictionary Creation

Create a simple dictionary with key-value pairs.

# Creating a basic dictionary

person = {

    "name": "Alice",

    "age": 30,

    "city": "New York"

}

print(person)

#Clcoding.com

{'name': 'Alice', 'age': 30, 'city': 'New York'}

Level 2: Accessing and Modifying values

Level 2: Accessing and Modifying Values Access values using keys, and modify existing key-value pairs. # Accessing values print(person["name"]) # Modifying values person["age"] = 31 print(person["age"]) #Clcoding.com Alice 31

Level 3: Adding and Removing key Values Pairs

Level 3: Adding and Removing Key-Value Pairs Add new key-value pairs and remove existing ones. # Adding a new key-value pair person["email"] = "alice@example.com" print(person) # Removing a key-value pair del person["city"] print(person) #Clcoding.com {'name': 'Alice', 'age': 31, 'city': 'New York', 'email': 'alice@example.com'} {'name': 'Alice', 'age': 31, 'email': 'alice@example.com'}
Level 4: Dictionary Methods

Level 4: Dictionary Methods Use dictionary methods like keys(), values(), items(), get(), and pop() # Getting all keys print(person.keys()) # Getting all values print(person.values()) # Getting all key-value pairs print(person.items()) # Using get() method print(person.get("name")) print(person.get("city", "Not Found")) # Using pop() method email = person.pop("email") print(email) print(person) dict_keys(['name', 'age', 'email']) dict_values(['Alice', 31, 'alice@example.com']) dict_items([('name', 'Alice'), ('age', 31), ('email', 'alice@example.com')]) Alice Not Found alice@example.com {'name': 'Alice', 'age': 31}

Level 5: Dictionary Comprehensions

Level 5: Dictionary Comprehensions

Create dictionaries using dictionary comprehensions for more concise and readable code.

# Dictionary comprehension

squares = {x: x*x for x in range(6)}

print(squares)

#Clcoding.com

{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Level 6: Nested Dictionary

Level 6: Nested Dictionaries

Work with dictionaries within dictionaries to represent more complex data structures.

# Nested dictionary

people = {

    "person1": {

        "name": "Alice",

        "age": 30

    },

    "person2": {

        "name": "Bob",

        "age": 25

    }

}

print(people)

# Accessing nested dictionary values

print(people["person1"]["name"])  

#Clcoding.com

{'person1': {'name': 'Alice', 'age': 30}, 'person2': {'name': 'Bob', 'age': 25}}

Alice

Level 7: Advanced Dictionary Operations

Level 7: Advanced Dictionary Operations Using advanced features like merging dictionaries, using defaultdict from collections, and performing operations with dict and zip # Merging dictionaries (Python 3.9+) dict1 = {"a": 1, "b": 2} dict2 = {"b": 3, "c": 4} merged_dict = dict1 | dict2 print(merged_dict) # Output: {'a': 1, 'b': 3, 'c': 4} # Using defaultdict from collections import defaultdict dd = defaultdict(int) dd["key1"] += 1 print(dd) # Output: defaultdict(<class 'int'>, {'key1': 1}) # Creating a dictionary from two lists using zip keys = ["name", "age", "city"] values = ["Charlie", 28, "Los Angeles"] person = dict(zip(keys, values)) print(person) #Clcoding.com {'a': 1, 'b': 3, 'c': 4} defaultdict(<class 'int'>, {'key1': 1}) {'name': 'Charlie', 'age': 28, 'city': 'Los Angeles'}

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5 Levels of Writing Python Classes

 

Level 1: Basic Class Creation and Instantiation

# Defining a basic class

class Dog:

    def __init__(self, name, age):

        self.name = name

        self.age = age

# Creating an instance

my_dog = Dog('Buddy', 3)

# Accessing attributes

print(my_dog.name)  

print(my_dog.age)   

#clcoding.com

Buddy

3

Level 2: Methods and Instance Variables

# Defining a class with methods

class Dog:

    def __init__(self, name, age):

        self.name = name

        self.age = age

    def bark(self):

        print(f"{self.name} says woof!")

# Creating an instance and calling a method

my_dog = Dog('Buddy', 3)

my_dog.bark()  

#clcoding.com

Buddy says woof!

Level 3: Class Variables and Class Methods

# Defining a class with class variables and methods

class Dog:

    species = 'Canis familiaris'  # Class variable

    def __init__(self, name, age):

        self.name = name

        self.age = age

    def bark(self):

        print(f"{self.name} says woof!")

    @classmethod

    def get_species(cls):

        return cls.species

# Accessing class variables and methods

print(Dog.species)  

print(Dog.get_species())  

#clcoding.com

Canis familiaris

Canis familiaris

Level 4: Inheritance and Method Overriding

# Base class and derived classes

class Animal:

    def __init__(self, name):

        self.name = name

    def speak(self):

        raise NotImplementedError("Subclasses must implement this method")

class Dog(Animal):

    def speak(self):

        return f"{self.name} says woof!"

class Cat(Animal):

    def speak(self):

        return f"{self.name} says meow!"

# Instances of derived classes

my_dog = Dog('Buddy')

my_cat = Cat('Whiskers')

# Calling overridden methods

print(my_dog.speak())  

print(my_cat.speak())  

#clcoding.com

Buddy says woof!

Whiskers says meow!

Level 5: Advanced Features (Polymorphism, Abstract Base Classes, Mixins)

from abc import ABC, abstractmethod

# Abstract base class

class Animal(ABC):

    @abstractmethod

    def speak(self):

        pass

# Derived classes implementing the abstract method

class Dog(Animal):

    def __init__(self, name):

        self.name = name

    def speak(self):

        return f"{self.name} says woof!"

class Cat(Animal):

    def __init__(self, name):

        self.name = name

    def speak(self):

        return f"{self.name} says meow!"

# Polymorphism in action

def animal_speak(animal):

    print(animal.speak())

# Creating instances

my_dog = Dog('Buddy')

my_cat = Cat('Whiskers')

# Using polymorphism

animal_speak(my_dog)  

animal_speak(my_cat)  

Buddy says woof!

Whiskers says meow!

 

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Python Coding challenge - Day 230 | What is the output of the following Python Code?

 

The code print('[' + chr(65) + ']') is a Python statement that prints a string to the console. Let's break down each part of this statement:

1. chr(65):
   • The chr() function in Python takes an integer (which represents a Unicode code point) and returns the corresponding character.
   • The integer 65 corresponds to the Unicode code point for the character 'A'.
   • So, chr(65) returns the character 'A'.

2. String Concatenation:

   • The + operator is used to concatenate strings in Python.
   • '[' + chr(65) + ']' concatenates three strings: the opening bracket '[', the character 'A' (which is the result of chr(65)), and the closing bracket ']'.
     
     
3. print():
   • The print() function outputs the concatenated string to the console.

Putting it all together, the statement print('[' + chr(65) + ']') prints the string [A] to the console.

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5 Levels of Writing Python Lists

 

Level 1: Basic List Creation and Access

# Creating a list

fruits = ['apple', 'banana', 'cherry']

# Accessing elements

print(fruits[0])  

# Adding an element

fruits.append('date')

print(fruits)  

# Removing an element

fruits.remove('banana')

print(fruits) 

#clcoding.com

apple

['apple', 'banana', 'cherry', 'date']

['apple', 'cherry', 'date']

Level 2: List Slicing and Iteration

# Slicing a list

print(fruits[1:3])  # Output: ['cherry', 'date']

# Iterating over a list

for fruit in fruits:

    print(fruit)

    

#clcoding.com

['cherry', 'date']

apple

cherry

date

Level 3: List Comprehensions

List comprehensions offer a concise way to create lists. They can replace loops for generating list elements.

# Creating a list of squares using list comprehension

squares = [x**2 for x in range(10)]

print(squares)  

#clcoding.com

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Level 4: Nested Lists and Multidimensional Arrays

# Creating a nested list (2D array)

matrix = [

    [1, 2, 3],

    [4, 5, 6],

    [7, 8, 9]

]

# Accessing elements in a nested list

print(matrix[1][2])  # Output: 6

# Iterating over a nested list

for row in matrix:

    for elem in row:

        print(elem, end=' ')

    print()

#clcoding.com

6

1 2 3 

4 5 6 

7 8 9 

Level 5: Advanced List Operations

# List comprehension with conditionals

even_squares = [x**2 for x in range(10) if x % 2 == 0]

print(even_squares)  

# Using higher-order functions: map, filter, and reduce

from functools import reduce

numbers = [1, 2, 3, 4, 5]

# Map: Apply a function to all items in the list

doubled = list(map(lambda x: x * 2, numbers))

print(doubled)  #

# Filter: Filter items based on a condition

even_numbers = list(filter(lambda x: x % 2 == 0, numbers))

print(even_numbers)  

#clcoding.com

[0, 4, 16, 36, 64]

[2, 4, 6, 8, 10]

[2, 4]

# Reduce: Reduce the list to a single value

sum_of_numbers = reduce(lambda x, y: x + y, numbers)

print(sum_of_numbers)  

# List references and copying

list1 = [1, 2, 3]

list2 = list1  # list2 is a reference to list1

list2.append(4)

print(list1)  

# Proper copying

list3 = list1.copy()

list3.append(5)

print(list1)  

print(list3)  

#clcoding.com

15

[1, 2, 3, 4]

[1, 2, 3, 4]

[1, 2, 3, 4, 5]

 

Read More

Demonstrating different types of colormaps

 

import matplotlib.pyplot as plt

import numpy as np

# Generate sample data

data = np.random.rand(10, 10)

# List of colormaps to demonstrate

colormaps = [

    'viridis',      # Sequential

    'plasma',       # Sequential

    'inferno',      # Sequential

    'magma',        # Sequential

    'cividis',      # Sequential

    'PiYG',         # Diverging

    'PRGn',         # Diverging

    'BrBG',         # Diverging

    'PuOr',         # Diverging

    'Set1',         # Qualitative

    'Set2',         # Qualitative

    'tab20',        # Qualitative

    'hsv',          # Cyclic

    'twilight',     # Cyclic

    'twilight_shifted' # Cyclic

]

# Create subplots to display colormaps

fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15, 20))

# Flatten axes array for easy iteration

axes = axes.flatten()

# Loop through colormaps and plot data

for ax, cmap in zip(axes, colormaps):

    im = ax.imshow(data, cmap=cmap)

    ax.set_title(cmap)

    plt.colorbar(im, ax=ax)

# Adjust layout to prevent overlap

plt.tight_layout()

# Show the plot

plt.show()

Explanation:

1. Generate Sample Data:

   data = np.random.rand(10, 10)

   This creates a 10x10 array of random numbers.

2. List of Colormaps:

   • A list of colormap names is defined. Each name corresponds to a different colormap in Matplotlib.

3. Create Subplots:

   fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15, 20))

   This creates a 5x3 grid of subplots to display multiple colormaps.

4. Loop Through Colormaps:

   • The loop iterates through each colormap, applying it to the sample data and plotting it in a subplot.

5. Add Colorbar:

   plt.colorbar(im, ax=ax)
   

   This adds a colorbar to each subplot to show the mapping of data values to colors.

6. Adjust Layout and Show Plot:

   plt.tight_layout() plt.show()

   These commands adjust the layout to prevent overlap and display the plot.

Choosing Colormaps

• Sequential: Good for data with a clear order or progression.
• Diverging: Best for data with a critical midpoint.
• Qualitative: Suitable for categorical data.
• Cyclic: Ideal for data that wraps around, such as angles.

By selecting appropriate colormaps, you can enhance the visual representation of your data, making it easier to understand and interpret.

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Python Coding challenge - Day 229 | What is the output of the following Python Code?

 

Code:

print('%x, %X' % (15, 15))

Solution and Explanation:

The code print('%x, %X' % (15, 15)) in Python uses string formatting to convert the integer 15 into hexadecimal format. Here is a step-by-step explanation:

1. String Formatting:

   • The % operator is used for formatting strings in Python. It allows you to embed values inside a string with specific formatting.

2. Format Specifiers:

   • %x and %X are format specifiers used for converting integers to their hexadecimal representation.
     • %x converts the integer to a lowercase hexadecimal string.
     • %X converts the integer to an uppercase hexadecimal string.

3. Tuple of Values:

   • The (15, 15) part is a tuple containing the values to be formatted. Each value in the tuple corresponds to a format specifier in the string.

4. Putting It All Together:

   • %x will take the first value from the tuple (15) and convert it to a lowercase hexadecimal string, which is f.
   • %X will take the second value from the tuple (also 15) and convert it to an uppercase hexadecimal string, which is F.

The resulting string will be "f, F", which is then printed to the console.

Here’s the breakdown of the code:

• %x -> f
• %X -> F

When you run the code print('%x, %X' % (15, 15)), it outputs: f, F

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Different Ways to Format and Print Characters in Python

 

Different Ways to Format and Print Characters in Python

1. Using f-strings (Python 3.6+)

print(f'[{chr(65)}]')

[A]

2. Using str.format method

print('[{}]'.format(chr(65)))

[A]

3. Using format function with placeholders

print('[{:c}]'.format(65))

[A]

4. Using concatenation with chr function

print('[' + chr(65) + ']')

[A]

5. Using old-style string formatting

print('[%c]' % 65)

[A]

Read More

Different Ways to Format and Print Hexadecimal Values in Python

 

Different Ways to Format and Print Hexadecimal Values in Python

1. Using f-strings (Python 3.6+)

print(f'{15:x}, {15:X}')

f, F

2. Using str.format method

print('{}, {}'.format(format(15, 'x'), format(15, 'X')))

f, F

3. Using format function with placeholders

print('{:x}, {:X}'.format(15, 15))

f, F

4. Using hex function and string slicing

print('{}, {}'.format(hex(15)[2:], hex(15)[2:].upper()))

f, F

5. Using old-style string formatting

print('%x, %X' % (15, 15))

f, F

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Different Ways to Print Binary Values as Decimals in Python

 

Different Ways to Print Binary Values as Decimals in Python

1. Directly using the binary literal

print(0b101)

5

2. Using int function with a binary string

print(int('101', 2))

5

3. Using format function for binary to decimal conversion

print(int(format(0b101, 'd')))

5

4. Using f-strings (Python 3.6+)

binary_value = 0b101

print(f'{binary_value}')

5

5. Using str.format method

binary_value = 0b101

print('{}'.format(binary_value))

5

Read More

Matrix in Python

 

Rank of Matrix

import numpy as np

x = np.matrix("4,5,16,7;2,-3,2,3;,3,4,5,6;4,7,8,9")

print(x)

[[ 4  5 16  7]

 [ 2 -3  2  3]

 [ 3  4  5  6]

 [ 4  7  8  9]]

#numpy.linalg.matrix_rank() - return a rank of a matrix

# Syntax: numpy.linalg.matrix_rank(matrix)

rank_matrix = np.linalg.matrix_rank(x)

print(rank_matrix)

4

Determinant of Matrix

import numpy as np

x = np.matrix("4,5,16,7;2,-3,2,3;,3,4,5,6;4,7,8,9")

print(x)

[[ 4  5 16  7]

 [ 2 -3  2  3]

 [ 3  4  5  6]

 [ 4  7  8  9]]

det_matrix = np.linalg.det(x)

print(det_matrix)

128.00000000000009

Inverse of a Matrix

inverse formula = A-1 = (1/determinant of A) * adj A

numpy.linalg.inv() - return the multiplicative inverse of a matrix Syntax: numpy.linalg.inv(matrix)

A = np.matrix("3,1,2;3,2,5;6,7,8")

print(A)

[[3 1 2]

 [3 2 5]

 [6 7 8]]

Inv_matrix = np.linalg.inv(A)

print(Inv_matrix)

[[ 0.57575758 -0.18181818 -0.03030303]

 [-0.18181818 -0.36363636  0.27272727]

 [-0.27272727  0.45454545 -0.09090909]]

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Python Coding challenge - Day 228 | What is the output of the following Python Code?

 

Code: 

list1 = [1]

list_iter = iter(list1)

print(next(list_iter))

Explanation:

Let's break down the code and its behavior step by step:

1. Creating the List:

   list1 = [1]

   This line creates a list named list1 containing a single element, the integer 1.

2. Creating the Iterator:

   list_iter = iter(list1)

   This line creates an iterator object from the list list1. The iter() function is used to create an iterator, which is an object that allows you to traverse through all the elements of a collection (such as a list) one by one.

3. Accessing the Next Element:

   print(next(list_iter))

   The next() function is used to retrieve the next item from the iterator list_iter. In this case, since the list list1 contains only one element, calling next(list_iter) will return the first and only element, which is 1.

Here is what happens in detail:

• When list_iter = iter(list1) is executed, an iterator object is created that will traverse the list list1.
• The first call to next(list_iter) retrieves the first element of list1, which is 1.
• The print() function then outputs this value to the console.

Putting it all together, the code:

list1 = [1]
list_iter = iter(list1)
print(next(list_iter))

will output:

1

This is because the iterator retrieves the first element from the list and print() prints it. If you were to call next(list_iter) again without adding more elements to the list or resetting the iterator, you would get a StopIteration exception since there are no more elements left in the iterator.

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Introduction to Network Automation

 

Build your subject-matter expertise

This course is part of the Network Automation Engineering Fundamentals 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 Free: Introduction to Network Automation

There are 3 modules in this course

The Network infrastructure industry has undergone a significant transformation in recent years, with an increasing need for automation due to factors such as a demand for faster and more reliable network deployments. Therefore, there is a growing need for network engineers skilled in automation and programmability.

This course is primarily intended for network engineers, systems engineers, network architects, and managers interested in learning the fundamentals of network automation.

By the end of the course, you will be able to:

- Articulate the role network automation and programmability plays in the context of end-to-end network management and operations.

- Interpret Python scripts with fundamental programming constructs built for network automation use cases.

To be successful in this course, you should be proficient in fundamental network routing & switching technologies, understand the basics of Python programming (3-6 mos exp.), and have some familiarity with Linux.

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Network Automation Engineering Fundamentals Specialization

 

What you'll learn

The issues network automation can solve, building a foundation for further mastery

The basics of NETCONF, RESTCONF, gNMI, and YANG modeling

How to script security topics with Ansible and Python

Join Free: Network Automation Engineering Fundamentals Specialization

Specialization - 5 course series

The Network Automation Engineering Fundamentals Specialization takes mid- to expert-level network engineers through the primary topics of network automation and programmability and prepares them for the NetDevOps environment. This Specialization serves as a well-rounded survey of topics and core skills that a network automation engineer should know to effectively deploy and operate a NetDevOps environment.

Completing this Specialization will help you prepare to operate as a network automation engineer with the skills needed to advance your career.

Applied Learning Project

We do not have any hands-on projects in this specialization curriculum. 

On Completion of this Specialization, you will be prepared to operate as a network automation engineer with the necessary skills needed to advance in your career. This Specialization serves as a well-rounded survey of topics and core skills that a network automation engineer should know to effectively deploy and operate a NetDevOps environment.

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Downloading a Audio file from YouTube Video

 

Code:

from pytube import YouTube

url = input('Enter a Video to Download Audio:')

yt = YouTube(url)

# Filter audio streams and download the first one

audio_stream = yt.streams.filter(only_audio=True).first()

audio_stream.download()

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Mastering PyTorch - Second Edition: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

 

Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples

Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks

Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features:

- Understand how to use PyTorch to build advanced neural network models

- Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker

- Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks

Book Description:

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most from your data and build complex neural network models.

You'll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You'll deploy PyTorch models to production, including mobile devices. Finally, you'll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai for prototyping models to training models using PyTorch Lightning. You'll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face.

By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

What You Will Learn:

- Implement text, vision, and music generating models using PyTorch

- Build a deep Q-network (DQN) model in PyTorch

- Deploy PyTorch models on mobile devices (Android and iOS)

- Become well-versed with rapid prototyping using PyTorch with fast.ai

- Perform neural architecture search effectively using AutoML

- Easily interpret machine learning models using Captum

- Design ResNets, LSTMs, and graph neural networks (GNNs)

- Create language and vision transformer models using Hugging Face

Who this book is for:

This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

Hard Copy: Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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Python Coding challenge - Day 227 | What is the output of the following Python Code?

 

Code:

s = "immutable"

s[1] = 'n'

print(s)

Solution and Explanation:

The code snippet you provided is attempting to demonstrate a fundamental property of Python strings: their immutability.

Let's break it down step by step:

Assigning the String:

s = "immutable"

Here, we assign the string "immutable" to the variable s. Strings in Python are sequences of characters and are immutable, meaning once a string is created, its content cannot be changed.

Attempting to Modify the String:

s[1] = 'n'

This line tries to change the character at index 1 of the string s from 'm' to 'n'. However, because strings are immutable, this operation is not allowed in Python. Attempting to assign a new value to a specific index of a string will raise a TypeError.

Printing the String:

print(s)

This line prints the current value of s. However, because the previous line raises an error, this line will not be executed unless the error is handled.

Let's see what happens if we run this code:

s = "immutable"

s[1] = 'n'  # This line will raise an error

print(s)  # This line will not be executed because of the error above

When you run this code, Python will raise an error at the line s[1] = 'n', and the error message will be something like this:

TypeError: 'str' object does not support item assignment

This error message indicates that you cannot assign a new value to an individual index in a string because strings do not support item assignment.

If you need to modify a string, you must create a new string. For example, if you want to change the second character of s to 'n', you can do it by creating a new string:

s = "immutable"

s = s[:1] + 'n' + s[2:]

print(s)

This code will correctly print innutable, as it constructs a new string by concatenating parts of the original string with the new character.

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Downloading a YouTube Playlist using Python

 

Code:

from pytube import Playlist

playlist_url = input('Enter Playlist:')

playlist = Playlist(playlist_url)

for video in playlist.videos:

    video.streams.get_highest_resolution().download()

#clcoding.com

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Machine Learning with Python: From Beginner to Advanced course syllabus

 

Module 1: Introduction to Machine Learning

• Week 1: Overview of Machine Learning

  • What is Machine Learning?
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement
  • Real-world applications of Machine Learning
  • Setting up Python environment: Anaconda, Jupyter Notebooks, essential libraries (NumPy, pandas, matplotlib, scikit-learn)

• Week 2: Python for Data Science

  • Python basics: Data types, control flow, functions
  • NumPy for numerical computing
  • pandas for data manipulation
  • Data visualization with matplotlib and seaborn

Module 2: Supervised Learning

• Week 3: Regression

  • Introduction to regression analysis
  • Simple Linear Regression
  • Multiple Linear Regression
  • Evaluation metrics: Mean Squared Error, R-squared

• Week 4: Classification

  • Introduction to classification
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC

• Week 5: Advanced Supervised Learning Algorithms

  • Decision Trees
  • Random Forests
  • Gradient Boosting Machines (XGBoost)
  • Support Vector Machines (SVM)

Module 3: Unsupervised Learning

• Week 6: Clustering

  • Introduction to clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN

• Week 7: Dimensionality Reduction

  • Introduction to dimensionality reduction
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • Singular Value Decomposition (SVD)

Module 4: Reinforcement Learning

• Week 8: Fundamentals of Reinforcement Learning

  • Introduction to Reinforcement Learning
  • Key concepts: Agents, Environments, Rewards
  • Markov Decision Processes (MDP)
  • Q-Learning

• Week 9: Deep Reinforcement Learning

  • Deep Q-Networks (DQN)
  • Policy Gradient Methods
  • Applications of Reinforcement Learning

Module 5: Deep Learning

• Week 10: Introduction to Neural Networks

  • Basics of Neural Networks
  • Activation Functions
  • Training Neural Networks: Forward and Backward Propagation

• Week 11: Convolutional Neural Networks (CNNs)

  • Introduction to CNNs
  • CNN architectures: LeNet, AlexNet, VGG, ResNet
  • Applications in Image Recognition

• Week 12: Recurrent Neural Networks (RNNs)

  • Introduction to RNNs
  • Long Short-Term Memory (LSTM) networks
  • Applications in Sequence Prediction

Module 6: Advanced Topics

• Week 13: Natural Language Processing (NLP)

  • Introduction to NLP
  • Text Preprocessing
  • Sentiment Analysis
  • Topic Modeling

• Week 14: Model Deployment and Production

  • Saving and loading models
  • Introduction to Flask for API creation
  • Deployment on cloud platforms (AWS, Google Cloud, Heroku)

• Week 15: Capstone Project

  • Work on a real-world project
  • End-to-end model development: Data collection, preprocessing, model training, evaluation, and deployment
  • Presentation and review

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Data Science Basics to Advance Course Syllabus

 

Week 1: Introduction to Data Science and Python Programming

• Overview of Data Science
  • Understanding what data science is and its importance.
• Python Basics
  • Introduction to Python, installation, setting up the development environment.
• Basic Python Syntax
  • Variables, data types, operators, expressions.
• Control Flow
  • Conditional statements, loops.
• Functions and Modules
  • Defining, calling, and importing functions and modules.
• Hands-on Exercises
  • Basic Python programs and assignments.

---

Week 2: Data Structures and File Handling in Python

• Data Structures
  • Lists, tuples, dictionaries, sets.
• Manipulating Data Structures
  • Indexing, slicing, operations.
• File Handling
  • Reading from and writing to files, file operations.
• Error Handling
  • Using try-except blocks.
• Practice Problems
  • Mini-projects involving data structures and file handling.

---

Week 3: Data Wrangling with Pandas

• Introduction to Pandas
  • Series and DataFrame objects.
• Data Manipulation
  • Indexing, selecting data, filtering.
• Data Cleaning
  • Handling missing values, data transformations.
• Data Integration
  • Merging, joining, concatenating DataFrames.
• Hands-on Exercises
  • Data wrangling with real datasets.

---

Week 4: Data Visualization

• Introduction to Matplotlib
  • Basic plotting, customization.
• Advanced Visualization with Seaborn
  • Statistical plots, customization.
• Interactive Visualization with Plotly
  • Creating interactive plots.
• Data Visualization Projects
  • Creating visualizations for real datasets.

---

Week 5: Exploratory Data Analysis (EDA) - Part 1

• Importance of EDA
  • Understanding data and deriving insights.
• Descriptive Statistics
  • Summary statistics, data distributions.
• Visualization for EDA
  • Histograms, box plots.
• Correlation Analysis
  • Finding relationships between variables.
• Hands-on Projects
  • Conducting EDA on real-world datasets.

---

Week 6: Exploratory Data Analysis (EDA) - Part 2

• Visualization for EDA
  • Scatter plots, pair plots.
• Handling Missing Values and Outliers
  • Techniques for dealing with incomplete data.
• Feature Engineering
  • Creating new features, transforming existing features.
• Hands-on Projects
  • Advanced EDA techniques on real datasets.

---

Week 7: Data Collection and Preprocessing Techniques

• Data Collection Methods
  • Surveys, web scraping, APIs.
• Data Cleaning
  • Handling missing data, outliers, and inconsistencies.
• Data Transformation
  • Normalization, standardization, encoding categorical variables.
• Hands-on Projects
  • Collecting and preprocessing real-world data.

---

Week 8: Database Management and SQL

• Introduction to Databases
  • Relational databases, database design.
• SQL Basics
  • SELECT, INSERT, UPDATE, DELETE statements.
• Advanced SQL
  • Joins, subqueries, window functions.
• Connecting Python to Databases
  • Using libraries like SQLAlchemy.
• Hands-on Exercises
  • SQL queries and database management projects.

---

Week 9: Introduction to Time Series Analysis

• Time Series Concepts
  • Understanding time series data, components of time series.
• Time Series Visualization
  • Plotting time series data, identifying patterns.
• Basic Time Series Analysis
  • Moving averages, smoothing techniques.
• Hands-on Exercises
  • Working with time series data.

---

Week 10: Advanced Time Series Analysis

• Decomposition
  • Breaking down time series into trend, seasonality, and residuals.
• Forecasting Methods
  • Introduction to ARIMA and other forecasting models.
• Model Evaluation
  • Assessing forecast accuracy.
• Practical Application
  • Time series forecasting projects.

---

Week 11: Advanced Data Wrangling with Pandas

• Advanced Data Manipulation
  • Pivot tables, groupby operations.
• Time Series Manipulation
  • Working with date and time data in Pandas.
• Merging and Joining DataFrames
  • Advanced techniques for combining datasets.
• Practical Exercises
  • Complex data wrangling tasks.

---

Week 12: Advanced Data Visualization Techniques

• Interactive Dashboards
  • Creating dashboards with Dash and Tableau.
• Geospatial Data Visualization
  • Mapping data with libraries like Folium.
• Storytelling with Data
  • Effective communication of data insights.
• Practical Projects
  • Building interactive and compelling data visualizations.

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