Day 25 : Python Program to find the prime factor of a number

 

def prime_factors(n):

    factors = []

    while n % 2 == 0:

        factors.append(2)

        n //= 2

    i = 3

    while i <= n:

        if n % i == 0:

            factors.append(i)

            n //= i

        else:

            i += 2  

    return factors

num = int(input("Enter a number: "))

factors = prime_factors(num)

print(f"Prime factors of {num} are: {factors}")

Code Explanation:

1. Function Definition

def prime_factors(n):

    factors = []

A function named prime_factors is defined to calculate the prime factors of a number n.

An empty list factors is initialized to store the prime factors.

2. Handle Factor of 2 (Even Prime)

while n % 2 == 0:

    factors.append(2)

    n //= 2

This loop continuously divides n by 2 as long as it is divisible by 2.

Each time n is divisible by 2:

2 is added to the factors list.

n is updated to n // 2 (integer division).

After this step, n becomes an odd number because all factors of 2 are removed.

3. Handle Odd Factors

i = 3

while i <= n:

A variable i is initialized to 3 to check for odd factors.

The loop runs as long as i is less than or equal to n.

if n % i == 0:

    factors.append(i)

    n //= i

Inside the loop:

If n is divisible by i, it adds i to the factors list and updates n to n // i.

This process removes all occurrences of the factor i.

else:

    i += 2

If n is not divisible by i, the next odd number (i + 2) is tested as a potential factor.

Only odd numbers are checked because all even factors are already removed.

4. Return the Factors

return factors

Once all factors are processed (i.e., n becomes 1), the function returns the list of prime factors.

5. User Input and Function Call

num = int(input("Enter a number: "))

The user is prompted to input an integer (num) for which the prime factors will be calculated.

factors = prime_factors(num)

print(f"Prime factors of {num} are: {factors}")

The prime_factors function is called with num as its argument.

The returned list of factors is stored in the variable factors and printed to the user.

#source code --> clcoding.com 

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Python List Quiz

 

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Python Coding challenge - Day 275| What is the output of the following Python Code?

Step-by-Step Explanation:

Line 1: Function Definition

def func(a, b=[]):

This defines a function func() that takes two arguments:

a: A required parameter.

b: An optional parameter with a default value of an empty list [].

Key Point: The default argument b=[] is evaluated only once at the time of function definition, and the same list object is reused across multiple calls to the function if not explicitly provided.

Line 2: Modify List

    b.append(a) 

The method append(a) adds the value of a to the list b.

Important Note: If the list b is shared across multiple calls due to the default argument, modifications will persist between calls.

Line 3: Return the List

    return b

The function returns the list b after appending the value of a to it.

Line 4: Call the function with 1

print(func(1))

When you call func(1), the default b=[] is used (an empty list at first).

The value 1 is appended to b.

The function returns [1].

[1]

Line 5: Call the function with 2

print(func(2))

Now, the function is called with func(2). The default list b is the same list object as the one used in the previous call.

The value 2 is appended to this same list.

The returned list now becomes [1, 2].

Output:

[1, 2]

 

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Python Coding challenge - Day 274| What is the output of the following Python Code?

Step-by-Step Explanation:

Line 1: Dictionary Comprehension

d = {x: x**2 for x in range(3)}

This is a dictionary comprehension.

Dictionary comprehensions are a concise way to create dictionaries in Python by specifying key-value pairs inside curly braces {}.

Components of the comprehension:

for x in range(3):

range(3) generates numbers from 0 to 2 (i.e., [0, 1, 2]).

The loop will iterate over these numbers: 0, 1, and 2.

x: x**2:

This specifies the key-value pair for the dictionary.

The key is x.

The value is x**2, which means the square of x.

How the comprehension works:

It will loop through the numbers 0, 1, and 2.

For each number x, it will compute x**2 and add the key-value pair to the dictionary.

Evaluating the comprehension step-by-step:

Here’s how the comprehension expands:

For x = 0:

Key = 0, Value = 0**2 = 0

Pair added: {0: 0}

For x = 1:

Key = 1, Value = 1**2 = 1

Pair added: {0: 0, 1: 1}

For x = 2:

Key = 2, Value = 2**2 = 4

Pair added: {0: 0, 1: 1, 2: 4}

Line 2: Print the dictionary

print(d)

This prints the dictionary d, which was created using the comprehension.

Output:

{0: 0, 1: 1, 2: 4}

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Python Coding challenge - Day 267 | What is the output of the following Python Code?

 

Code Explanation:

my_dict = {}

my_dict[[1, 2, 3]] = "value"

print(my_dict)

Step-by-Step Explanation:

Dictionary Creation:

my_dict = {}

An empty dictionary my_dict is created. At this point, it contains no key-value pairs.

Attempt to Add a Key-Value Pair:

my_dict[[1, 2, 3]] = "value"

Here, you are trying to use a list [1, 2, 3] as a key in the dictionary.

In Python, dictionary keys must be hashable (immutable and capable of producing a consistent hash value). Lists, however, are mutable and therefore not hashable.

As a result, this operation raises a TypeError with the message:

TypeError: unhashable type: 'list'.

Print Statement:

print(my_dict)

This line is never executed because the code raises an error at the previous step.

Why Lists Cannot Be Keys:

Lists in Python are mutable, meaning their contents can change (e.g., adding/removing elements). If lists were allowed as dictionary keys, the hash value of the key could change after it was added to the dictionary, leading to unpredictable behavior.

To fix this, you could use an immutable tuple instead of a list as the key:

my_dict[(1, 2, 3)] = "value"

print(my_dict)

Output:

{(1, 2, 3): 'value'}

Final Outcome:

The original code raises a TypeError because lists are not hashable and cannot be used as dictionary keys.

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Python Coding challenge - Day 268| What is the output of the following Python Code?

 Step-by-Step Explanation:

list1 = [1, 2, 3]:

A list [1, 2, 3] is created and assigned to list1.

list2 = list1:

list2 is assigned to reference the same list object as list1.

Both list1 and list2 now refer to the same object in memory.

list2.append(4):

The append(4) method modifies the list in place, adding the value 4 to the end of the list.

Since list1 and list2 refer to the same list, this change is reflected in both variables.

print(list1):

The list now contains [1, 2, 3, 4] because the modification affected the original list object.

Final Output:

[1, 2, 3, 4]

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Python Coding challenge - Day 270| What is the output of the following Python Code?

Code Explanation:

1. print(True == 1)

In Python, the bool type (True and False) is a subclass of the int type.

True is internally represented as the integer 1, and False is represented as the integer 0.

The equality check True == 1 evaluates to True because they are effectively the same in Python.

Output: True

2. print(False == 0)

Similarly, False is represented as the integer 0 in Python.

The equality check False == 0 evaluates to True because they are equivalent.

Output: True

3. print(True + True)

Since True is equivalent to 1, adding True + True is the same as adding 1 + 1.

The result of this addition is 2.

Output: 2

Final Output:

True

True

2

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Python Coding challenge - Day 273| What is the output of the following Python Code?

Step-by-Step Explanation:

Line 1:

x = [1, 2, 3, 4]

A list [1, 2, 3, 4] is created and assigned to the variable x.

In Python, lists are mutable, which means their elements can be changed after creation.

Line 2:

y = x

The variable y is assigned the reference to the same list as x.

Both x and y now point to the same memory location in Python's memory.

Line 3:

y[0] = 99

This modifies the first element (index 0) of the list y. Since y is referencing the same memory location as x, this change is reflected in x as well.

So now, the list becomes [99, 2, 3, 4].

Line 4:

print(x)

The print() function outputs the current state of x.

Because y[0] = 99 also changed the first element of x, the output will show [99, 2, 3, 4].

Output:

[99, 2, 3, 4]

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Python Coding challenge - Day 271| What is the output of the following Python Code?

 

Step-by-Step Explanation:

Line 1:

x = [1, 2, 3]

A list [1, 2, 3] is created and assigned to the variable x.

Line 2:

y = x

The variable y now points to the same list object as x. Both x and y reference the same memory location.

Line 3:

x = x + [4]

This creates a new list by concatenating [1, 2, 3] (the value of x) with [4].

The new list [1, 2, 3, 4] is assigned to x.

Now x references the new list [1, 2, 3, 4], while y still references the original list [1, 2, 3].

Line 4:

print(y)

Since y still points to the original list, the output is [1, 2, 3].

Output:

[1, 2, 3]

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Python Coding challenge - Day 272| What is the output of the following Python Code?

 

Step-by-Step Explanation:

Line 1:

x = (1, 2, 3)

A tuple (1, 2, 3) is created and assigned to the variable x. Tuples are immutable in Python, meaning their elements cannot be changed after creation.

Line 2:

y = x

The variable y is assigned the same reference as x. At this point, both x and y point to the same memory location.

Line 3:

y += (4,)

The += operator here is used to concatenate the tuple (4,) to the tuple referenced by y.

However, tuples are immutable in Python. This means that you cannot directly modify the contents of a tuple using an operation like +=.

When you perform y += (4,), Python creates a new tuple by concatenating the original tuple (1, 2, 3) with (4,).

y is now pointing to this new tuple (1, 2, 3, 4).

Important Note: This does not modify x because tuples are immutable. x still points to the original tuple (1, 2, 3).

Line 4:

print(x)

This prints the original tuple x. Since x was never modified (because tuples are immutable), it still refers to the original value (1, 2, 3).

Output:

(1, 2, 3)

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Python Coding challenge - Day 2| What is the output of the following Python Code?

 

Step-by-Step Explanation:

Function Definition:

def uppercase_text(text):

 return text.upper()

This defines a function called uppercase_text that takes a single parameter, text.

Inside the function, the upper() method is called on the string passed as the text argument.

The upper() method converts all lowercase letters in the string to uppercase and returns the result.

Function Call:

result = uppercase_text("Hello, world!")

The function uppercase_text is called with the string "Hello, world!" as an argument.

Inside the function:

The string "Hello, world!" is converted to "HELLO, WORLD!" using the upper() method.

The function returns the uppercase string "HELLO, WORLD!".

The returned value is assigned to the variable result.

Printing the Result:

print(result)

The print() function outputs the value of the variable result, which is "HELLO, WORLD!".

Output:

HELLO, WORLD!

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Python Coding Challange - Question With Answer

                                                                                                                                                                    

data = {'A': [1, 2], 'B': [3, 4]}
df = pd.DataFrame(data)
print(df.shape)

Explanation:

data = {'A': [1, 2], 'B': [3, 4]}

1. A dictionary named data is created with two key-value pairs:
   • • Key 'A' maps to the list [1, 2]
     • Key 'B' maps to the list [3, 4]
   • This dictionary represents column data for a table:

     
     A  B
     1  3
     2  4

---

2. df = pd.DataFrame(data)
   • A Pandas DataFrame is created from the dictionary.
   • Each key in the dictionary ('A' and 'B') becomes a column in the DataFrame.
   • The values in the lists ([1, 2] for 'A' and [3, 4] for 'B') become the rows under the respective columns.
   • The resulting DataFrame looks like this:

     
        A  B
     0   1  3
     1   2  4

---

3. print(df.shape)
   • The shape attribute of a DataFrame returns a tuple representing its dimensions:
     (number_of_rows, number_of_columns)
   • For this DataFrame:
     • Number of rows = 2 (index 0 and 1)
     • Number of columns = 2 ('A' and 'B')
   • df.shape returns (2, 2).

---

Final Output:

(2, 2)

This tells us the DataFrame has 2 rows and 2 columns.

 

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Day 24 : Python Program to print in a range without using loop

 

def print_range(start, end):

    if start > end:

        return

    print(start)

    print_range(start + 1, end)

start = int(input("Enter the starting number: "))

end = int(input("Enter the ending number: "))

print_range(start, end)

Code Explanation:

1. Function Definition

def print_range(start, end):

A function named print_range is defined.

It takes two arguments:

start: The starting number.

end: The ending number.

2. Base Case for Recursion

if start > end:

    return

This is the base case of the recursion, which stops the function from continuing further:

If start is greater than end, the function simply returns without doing anything.

3. Print the Current Number

print(start)

If start is not greater than end, the current value of start is printed.

4. Recursive Call

print_range(start + 1, end)

After printing the current number, the function calls itself with:

start + 1: The next number in the sequence.

end: The same ending number.

This continues until the base case (start > end) is met.

5. Input from User

start = int(input("Enter the starting number: "))

end = int(input("Enter the ending number: "))

The user is prompted to input the start and end values for the range.

int(input(...)) ensures the inputs are converted to integers.

6. Call the Function

print_range(start, end)

The print_range function is called with the user-provided start and end values.

Recursive Process (How it Works)

The function prints the current value of start.

It calls itself with the next number (start + 1).

This continues until start > end, at which point the function stops

source code --> clcoding.com 

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Day 23 : Python Program to Print All Odd Numbers in a given Range

 start = int(input("Enter the start of the range: "))

end = int(input("Enter the end of the range: "))

print("Odd numbers between", start, "and", end, "are:")

for num in range(start, end + 1):

    if num % 2 != 0: 

        print(num, end=" ")  

Code Explanation:

1. Prompt User for Input:

start = int(input("Enter the start of the range: "))

end = int(input("Enter the end of the range: "))

The program prompts the user to input two integers:

start: The beginning of the range.

end: The end of the range.

int(input(...)) ensures the input is converted from a string to an integer.

2. Print a Header:

print("Odd numbers between", start, "and", end, "are:")

This line prints a message specifying the range of numbers being analyzed.

3. Loop Through the Range:

for num in range(start, end + 1):

range(start, end + 1) generates all integers from start to end (inclusive).

The loop iterates through each number in this range, assigning it to the variable num.

4. Check for Odd Numbers:

if num % 2 != 0:

This checks whether num is odd:

num % 2: Computes the remainder when num is divided by 2.

!= 0: Ensures the remainder is not zero (odd numbers always leave a remainder of 1).

5. Print Odd Numbers:

print(num, end=" ")

If num is odd, it is printed on the same line.

end=" " ensures the numbers are printed with a space in between, instead of a new line after each.

#source code --> clcoding.com

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Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

 

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks 

Deep learning has revolutionized the field of artificial intelligence by enabling machines to learn complex patterns and perform tasks once considered exclusive to humans. This book serves as a comprehensive guide to understanding and implementing deep learning systems, blending theoretical foundations with hands-on applications using two of the most popular frameworks: PyTorch and TensorFlow.

The book begins by introducing the core principles of neural networks, the backbone of deep learning. It then explores the evolution of machine learning systems, emphasizing the role of architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and generative adversarial networks (GANs). By the end, readers will have a solid grasp of how these technologies power applications such as image recognition, natural language processing (NLP), and generative modeling.

Whether you're a beginner stepping into AI or a practitioner looking to enhance your skills, this book provides the knowledge and tools needed to build and optimize state-of-the-art machine learning systems.

Dive into the core of deep learning and machine learning with this hands-on guide that provides a solid foundation for anyone from data scientists to AI enthusiasts. This book, meticulously structured for clarity and depth, unravels the mysteries of neural networks, large language models (LLMs), and generative AI. With clear explanations and a focus on practical applications, it’s your ultimate resource for mastering machine learning with Python.

What You’ll Learn Inside:

Foundations of Machine Learning and Deep Learning

Discover why machines learn the way they do and understand the algorithms that power modern machine learning models. Explore the evolution of AI, from basic network structures to sophisticated LLMs and RAG (retrieval-augmented generation) techniques.

Practical Model Building with PyTorch and TensorFlow

Get hands-on experience with Python programming, PyTorch, and TensorFlow—the most powerful tools in machine learning system design. Learn to build and optimize models that solve real-world problems, from NLP (Natural Language Processing) with Transformers to generative deep learning for image synthesis.

Advanced Techniques for Model Optimization and System Design

Master the art of hyperparameter tuning, data preprocessing, and system design for deep learning. This book also introduces GitHub and version control for efficient model management, essential for any data-driven project.

Real-World Applications

Whether you’re interested in algorithmic trading, hands-on machine learning with scikit-learn, Keras, and TensorFlow, or understanding deep learning for natural language processing, this book covers it all. See how deep learning with PyTorch and machine learning with Python apply across fields, from data science to cutting-edge generative AI.

Perfect for readers who want to build expertise in machine learning engineering, this guide also delves into the math behind neural networks, numpy, and Python pandas—everything you need to build robust learning systems from scratch. Whether you’re a seasoned programmer or new to AI, Understanding Deep Learning will equip you with the tools and knowledge to make an impact in the world of AI.

Hard Copy: Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

Kindle: Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

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Machine Learning Evaluation: Towards Reliable and Responsible AI

 

Machine Learning Evaluation: Towards Reliable and Responsible AI

This book delves into the critical yet often overlooked aspect of evaluating machine learning (ML) models and systems. As artificial intelligence becomes increasingly integrated into decision-making processes across industries, ensuring that these systems are reliable, robust, and ethically sound is paramount. The book provides a comprehensive framework for evaluating machine learning models, with a strong focus on developing systems that are both reliable and responsible.

As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website.

Key Themes of the Book

1. Importance of Evaluation in Machine Learning

The book begins by emphasizing the need for rigorous evaluation of ML models, explaining:

Why evaluation is a cornerstone for reliable AI.

The limitations of traditional metrics like accuracy, precision, recall, and F1 score, especially in complex real-world scenarios.

How poor evaluation can lead to unreliable models and ethical issues, such as bias, unfairness, and unintended consequences.

2. Dimensions of Machine Learning Evaluation

Evaluation is not just about measuring performance but also about assessing broader dimensions, including:

Model Robustness: Ensuring models perform well under varying conditions, such as noisy data or adversarial attacks.

Generalizability: Testing the model on unseen or out-of-distribution data.

Fairness: Identifying and mitigating biases that could result in discriminatory outcomes.

Explainability and Interpretability: Ensuring that the model's decisions can be understood and justified.

Sustainability: Considering the computational and environmental costs of training and deploying models.

3. Types of Evaluation Metrics

The book explores various types of metrics, their strengths, and their limitations:

Standard Metrics: Accuracy, precision, recall, ROC-AUC, and their applicability in classification, regression, and clustering problems.

Task-Specific Metrics: Metrics tailored for domains like natural language processing (e.g., BLEU for translation, perplexity for language models) or computer vision (e.g., Intersection over Union (IoU) for object detection).

Ethical Metrics: Measuring fairness (e.g., demographic parity, equalized odds) and trustworthiness.

4. Evaluating Model Reliability

To ensure a model’s reliability, the book discusses:

Robustness Testing: How to test models under adversarial attacks, noisy inputs, or rare events.

Stress Testing: Evaluating performance in edge cases or extreme conditions.

Error Analysis: Techniques for identifying and diagnosing sources of errors.

5. Evaluating Responsible AI

The book takes a deep dive into what it means for AI to be responsible, addressing:

Fairness in AI:

Methods for detecting and reducing bias in datasets and algorithms.

Case studies showing how fairness issues can harm users and organizations.

Transparency and Explainability:

Tools and frameworks (e.g., SHAP, LIME) for understanding and explaining model predictions.

Importance of explainability in high-stakes domains like healthcare and finance.

Ethical Decision-Making:

Balancing performance with societal impact.

Guidelines for aligning AI development with ethical principles.

Hard Copy: Machine Learning Evaluation: Towards Reliable and Responsible AI

Kindle: Machine Learning Evaluation: Towards Reliable and Responsible AI

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Steps to Beginner's Machine Learning: The Ultimate Beginner’s Guide to Understanding AI and Machine Learning Concepts

 

Step into the Future with Machine Learning – The Ultimate Beginner’s Guide to Understanding AI and Machine Learning Concepts

Are you curious about Artificial Intelligence but unsure where to start? Steps to Beginner's Machine Learning: The Ultimate Beginner’s Guide to Understanding AI and Machine Learning Concepts is the perfect launchpad for anyone eager to dive into the world of AI, even if they have no prior technical experience. Whether you're a student, a professional, or simply someone with an interest in cutting-edge technology, this book is designed to break down complex concepts into easy-to-understand, actionable steps.

What’s Inside:

This guide takes you on a journey from the very basics to a deeper understanding of machine learning. It begins by explaining what AI and machine learning are, how they work, and how they’re shaping the world around us. Through engaging examples and simple analogies, you'll learn about the core principles and foundational techniques used by data scientists and engineers. Each chapter is packed with clear explanations, hands-on exercises, and real-world examples to ensure you not only grasp the theory but also gain the practical skills you need to start applying machine learning concepts.

The book covers:

What is Machine Learning? - An introduction to the key concepts and terminology.

Supervised vs. Unsupervised Learning - Understanding the types of machine learning and how to choose between them.

Data Preprocessing and Cleaning - How to prepare your data for machine learning algorithms.

Popular Algorithms - An introduction to algorithms like Linear Regression, Decision Trees, and K-means Clustering.

Evaluating Models - Learn how to assess the performance of your models using metrics like accuracy, precision, and recall.

Hands-On Projects - Work on practical exercises that let you apply what you’ve learned to real-world datasets.

Why This Book?

Unlike other technical books that are filled with jargon and overwhelming explanations, Steps to Beginner’s Machine Learning simplifies learning and makes AI and machine learning accessible for everyone. The book uses practical examples, step-by-step guides, and illustrations to ensure that learning is interactive and fun.

If you’re ready to enter the world of machine learning but don’t know where to begin, this book will give you the knowledge and confidence to take the first step. Start your AI journey today and unlock the door to endless possibilities!

Perfect For:

Complete beginners to AI and machine learning

Students looking for a solid introduction to machine learning

Professionals seeking to understand machine learning concepts in a simple way

Hard Copy: Steps to Beginner's Machine Learning: The Ultimate Beginner’s Guide to Understanding AI and Machine Learning Concepts

Kindle: Steps to Beginner's Machine Learning: The Ultimate Beginner’s Guide to Understanding AI and Machine Learning Concepts

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Large Language Model Crash Course: Hands on With Python (Mastering Machine Learning)

 

Large Language Model Crash Course: Hands on With Python (Mastering Machine Learning)

In recent years, large language models (LLMs) have emerged as a transformative force in artificial intelligence, powering applications such as conversational AI, text generation, summarization, and more. This book, "Large Language Model Crash Course: Hands-On with Python (Mastering Machine Learning)", offers a practical and accessible guide to understanding and implementing LLMs using Python.

The book is designed for learners and practitioners who want to explore the mechanics, capabilities, and applications of cutting-edge language models, such as GPT (Generative Pre-trained Transformer). By bridging theory with hands-on exercises, it demystifies the underlying technologies, including transformers, attention mechanisms, and fine-tuning techniques, while focusing on their real-world applications.

Through Python-based examples and projects, readers will learn how to build, train, and deploy language models efficiently. Additionally, the book delves into challenges like handling large datasets, optimizing performance, ensuring ethical AI use, and mitigating biases in LLMs. Whether you're an AI enthusiast, data scientist, or developer, this crash course provides the essential tools to master the rapidly evolving field of large language models.

Unlock the full potential of Natural Language Processing (NLP) with the definitive guide to Large Language Models (LLMs)! This comprehensive resource is perfect for beginners and seasoned professionals alike, revealing the intricacies of state-of-the-art NLP models. Dive into a wealth of knowledge packed with theoretical insights, practical examples, and Python code to implement key concepts. Experience firsthand the transformative power LLMs can have on a variety of applications spanning diverse industries.

Key Features:

Comprehensive coverage—from foundational NLP concepts to advanced model architectures.

Detailed exploration of pre-training, fine-tuning, and deploying LLMs.

Hands-on Python code examples for each chapter.

SEO-optimized knowledge that encompasses a wide array of tasks and capabilities in NLP.

What You Will Learn:

• Grasp the basics with an introduction to Large Language Models and their influence on NLP.
• Delve into the essentials of NLP fundamentals critical for LLM comprehension.
• Analyze traditional language models, including their mechanisms and limitations.
• Discover the power of word embeddings such as Word2Vec and GloVe.
• Explore how deep learning catalyzed a revolution in natural language processing.
• Understand the structure and functionality of neural networks relevant to NLP.
• Master Recurrent Neural Networks (RNNs) and their applications in text processing.
• Navigate the workings of Long Short-Term Memory (LSTM) networks for long-term text dependencies.
• Appreciate the transformative impact of the Transformer architecture on NLP.
• Learn the importance of attention mechanisms and self-attention in modern LLMs.
• Decode the architecture and function of the BERT model in NLP tasks.
• Trace the evolution and design of GPT models from GPT to GPT-4.
• Explore pre-training methodologies that underpin large-scale language models.
• Fine-tune LLMs for specific applications with precision and effectiveness.
• Innovate with generative model fine-tuning for creative text generation tasks.
• Optimize models through contrastive learning for superior performance.
• Excavate the nuances of in-context learning techniques in LLMs.
• Apply transfer learning principles to enhance language model capabilities.
• Comprehend the nuances of training LLMs from a technical standpoint.
• Prepare datasets meticulously for language model training success.

Hard Copy: Large Language Model Crash Course: Hands on With Python (Mastering Machine Learning)

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Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications

Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications

Machine learning and artificial intelligence are ubiquitous terms for improving technical processes. However, practical implementation in real-world problems is often difficult and complex.

This textbook explains learning methods based on analytical concepts in conjunction with complete programming examples in Python, always referring to real technical application scenarios. It demonstrates the use of physics-informed learning strategies, the incorporation of uncertainty into modeling, and the development of explainable, trustworthy artificial intelligence with the help of specialized databases.

Therefore, this textbook is aimed at students of engineering, natural science, medicine, and business administration as well as practitioners from industry (especially data scientists), developers of expert databases, and software developers.

This book bridges the gap between traditional engineering disciplines and modern machine learning (ML) techniques, offering a comprehensive introduction to how AI can solve complex engineering problems. With a focus on physics-informed machine learning and explainable AI (XAI), it aims to equip engineers with the skills to integrate data-driven approaches into their workflows while respecting the principles of engineering systems.

Key Themes of the Book

1. The Role of Machine Learning in Engineering

Why Engineers Need Machine Learning:

Traditional computational methods often struggle with high-dimensional problems, noisy data, and real-time predictions.

ML provides powerful tools to model complex systems, optimize processes, and predict outcomes with greater accuracy.

Challenges in Engineering Applications:

Integration of domain knowledge (e.g., physics laws) into ML.

The need for models that are not only accurate but also interpretable and trustworthy.

2. Introduction to Physics-Informed Machine Learning

Physics-informed machine learning (PIML) integrates known physical laws (e.g., conservation laws, boundary conditions) into the learning process, ensuring that ML models respect underlying physical principles.

What is PIML?

Combines data-driven methods with first-principle physics models.

Useful for problems with limited data but strong domain constraints.

Applications of PIML:

Computational fluid dynamics (CFD).

Structural health monitoring.

Material design and optimization.

Techniques in PIML:

Physics-Informed Neural Networks (PINNs): Incorporates partial differential equations (PDEs) as loss functions.

Hybrid Models: Combines machine learning with physics-based simulations.

3. Explainable AI (XAI) for Engineers

Why Explainability Matters:

Engineers need to trust and understand ML models, especially for safety-critical systems (e.g., aviation, power grids).

Regulatory and ethical considerations demand transparency.

Explainability Techniques:

Post-hoc methods: Tools like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations).

Intrinsic interpretability: Using simpler models like decision trees or physics-guided architectures.

Case Studies:

Explaining material failure predictions.

Interpreting predictive maintenance models for mechanical systems.

4. Machine Learning Techniques for Engineering Applications

The book explores ML algorithms tailored to engineering use cases:

Supervised Learning:

Regression and classification for failure prediction and fault detection.

Unsupervised Learning:

Clustering and anomaly detection in sensor data.

Deep Learning:

Neural networks for modeling complex relationships in structural analysis and fluid mechanics.

Reinforcement Learning:

Optimizing control systems for robotics and autonomous vehicles.

5. Practical Implementation Using Python

The book emphasizes hands-on learning through Python-based examples and tutorials:

Popular Libraries:

TensorFlow and PyTorch for model development.

Scikit-learn for classical ML techniques.

Specialized libraries like SimPy for simulation modeling and OpenFOAM for CFD integration.

Building Physics-Informed Models:

Examples of integrating physics constraints into neural network training.

Model Deployment:

Techniques for deploying ML models in real-time engineering systems.

Hard Copy: Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications

 

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MACHINE LEARNING AND C# CODING MADE SIMPLE: A BEGINNER’S GUIDE TO PROGRAMMING - 2 BOOKS IN 1

 

This book combines two essential topics—machine learning and programming with C#—to provide a comprehensive introduction for beginners. By merging foundational concepts in machine learning with hands-on coding tutorials, the book aims to help readers develop both a theoretical understanding of AI and practical skills in building applications using the C# programming language.

MACHINE LEARNING MADE SIMPLE is an extensive and insightful guide that takes you on a journey through the exciting world of machine learning. From the fundamentals to advanced topics, this book equips you with the knowledge and understanding needed to navigate the complexities of machine learning and its ethical implications.

With a strong focus on ethics, bias, and responsible AI, this book goes beyond the technical aspects of machine learning algorithms. It explores the societal impact of AI systems and addresses the critical considerations of fairness, transparency, and accountability in their development and deployment. You'll gain a deep understanding of the potential risks and challenges associated with machine learning, along with practical strategies to mitigate bias and ensure ethical decision-making.

Each chapter of Machine Learning Unleashed is carefully crafted to provide comprehensive explanations, detailed examples, and algorithmic details, enabling both beginners and experienced practitioners to grasp the concepts effectively. You'll explore diverse topics such as neural networks, deep learning, reinforcement learning, and natural language processing, all presented with clarity and real-world relevance.

Whether you're an aspiring data scientist, a machine learning enthusiast, or a technology professional, this book will empower you to:

- Gain a solid understanding of machine learning fundamentals and techniques

- Navigate the ethical considerations and biases present in machine learning algorithms

- Learn how to mitigate bias and promote fairness in model development and deployment

- Discover the practical applications of machine learning in various domains

- Grasp advanced concepts like deep learning, reinforcement learning, and natural language processing

- Develop a responsible and ethical approach to AI development and deployment

Programming is an essential skill in today's digital age, and if you're looking to learn a powerful and versatile language, C# should be at the top of your list. In this book, we'll dive into the world of C# programming. By the end, you'll have a solid foundation in C# and be ready to tackle your own programming projects.

Have you ever wondered how computer programs are created? How applications and software work seamlessly to perform complex tasks? Learning a programming language like C# can unlock a world of possibilities and empower you to create your own applications, games, and software solutions. Whether you're a beginner with no programming experience or an experienced developer looking to expand your skillset, this book will guide you through the process of learning C# from scratch.

What Makes This Book Unique?

Two-in-One Approach:

Combines the theory of machine learning with the practical skills of C# programming, making it ideal for beginners in both fields.

Hands-On Examples:

Step-by-step projects to build machine learning models and integrate them into applications using C#.

Beginner-Friendly Language:

Avoids heavy jargon, ensuring concepts are accessible to readers with no prior experience.

Who Should Read This Book?

Beginners in Programming: People who want to learn coding with C# from scratch.

Aspiring Data Scientists and AI Enthusiasts: Those who want to explore machine learning using a versatile programming language like C#.

C# Developers: Developers familiar with C# who want to expand their skills into machine learning.

Hobbyists: Readers interested in creating AI-powered applications as a personal project.

Learning Outcomes

By the end of the book, readers will:

Understand the fundamentals of machine learning and its applications.

Learn to program in C#, covering both basic and advanced concepts.

Build and implement machine learning models using C# and ML.NET.

Gain confidence in applying machine learning concepts to solve real-world problems.

Kindle: MACHINE LEARNING AND C# CODING MADE SIMPLE: A BEGINNER’S GUIDE TO PROGRAMMING - 2 BOOKS IN 1

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Python Coding challenge - Day 1| What is the output of the following Python Code?

 

 Code Explanation:

1. my_dict = {"a": 1, "b": 2, "c": 3}

This line creates a dictionary named my_dict with 3 key-value pairs:

Key "a" maps to the value 1.

Key "b" maps to the value 2.

Key "c" maps to the value 3.

So, the dictionary looks like this:

my_dict = {"a": 1, "b": 2, "c": 3}

2. result = my_dict.values()

The values() method returns a view object that displays a dynamic view over the dictionary's values. Here:

result = my_dict.values()

my_dict.values() returns all the values in the dictionary my_dict.

It returns a special type called dict_values. This is not a list but a view object.

The contents are: [1, 2, 3], but represented by the view dict_values.

So the view object contains the values:

dict_values([1, 2, 3])

The dict_values object will dynamically reflect changes in the dictionary if they occur.

3. print(result)

This line prints the dict_values view object.

print(result)

dict_values([1, 2, 3])

Output with the above:

[1, 2, 3]

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Python Coding challenge - Day 269| What is the output of the following Python Code?

 

 Code Explanation:

range(3):

The range() function generates a sequence of numbers starting from 0 by default and stopping before the given number (3 in this case). So, range(3) generates the numbers: 0, 1, 2.

for i in range(3)::

This is a for loop that iterates over each number in the sequence produced by range(3) (0, 1, and 2).

In each iteration, the variable i takes on the next value from the sequence.

print(i, end=", "):

The print() function outputs the value of i during each iteration.

The end=", " argument specifies what to append at the end of each print statement instead of the default newline (\n). In this case, it appends a comma followed by a space , .

Output Generation:

On the first iteration, i = 0, so it prints 0, .

On the second iteration, i = 1, so it prints 1, .

On the third iteration, i = 2, so it prints 2, .

Final Output:

0, 1, 2, 

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Day 22 : Python Program to Print All Integers that are 'not Divisible by Either 2 or 3

 

def print_not_divisible_by_2_or_3(start, end):

    print(f"Integers between {start} and {end} not divisible by 2 or 3:")

    for num in range(start, end + 1):

          if num % 2 != 0 and num % 3 != 0:

            print(num, end=' ')

    print()  

start = int(input("Enter the start of the range: "))

end = int(input("Enter the end of the range: "))

print_not_divisible_by_2_or_3(start, end)

Code Explanation:

1. Function Definition:

def print_not_divisible_by_2_or_3(start, end):

This defines a function named print_not_divisible_by_2_or_3, which takes two arguments:

start: The starting number of the range.

end: The ending number of the range.

2. Print a Header:

print(f"Integers between {start} and {end} not divisible by 2 or 3:")

This line prints a message indicating the range of numbers that will be checked.

3. Loop Through the Range:

for num in range(start, end + 1):

This loop iterates through all integers from start to end (inclusive).

range(start, end + 1) generates the sequence of numbers within this range.

4. Check the Condition:

if num % 2 != 0 and num % 3 != 0:

num % 2 != 0: Checks if the number is not divisible by 2 (i.e., it's odd).

num % 3 != 0: Checks if the number is not divisible by 3.

The condition ensures the number meets both criteria (not divisible by either 2 or 3).

5. Print Numbers Meeting the Condition:

print(num, end=' ')

If the condition is met, the number is printed on the same line.

end=' ' ensures the numbers are printed with a space instead of a newline after each.

6. End of Function:

print()

After the loop completes, this adds a newline to separate the output from subsequent program output.

Main Program:

start = int(input("Enter the start of the range: "))

end = int(input("Enter the end of the range: "))

The user is prompted to input the starting (start) and ending (end) numbers of the range.

int(input(...)) ensures the input is converted from a string to an integer.

Call the Function:

print_not_divisible_by_2_or_3(start, end)

The function is called with start and end as arguments, performing the checks and printing the results.

#source code --> clcoding.com 

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Python Coding challenge - Day 266 | What is the output of the following Python Code?

 

Code Explanation:

a, b, c = (1, 2, 3)

This is tuple unpacking. The tuple (1, 2, 3) is unpacked, assigning:

a = 1

b = 2

c = 3

a, b = b, a + b

This is another unpacking operation. The right-hand side of the assignment b, a + b is evaluated first, and then assigned to a and b:

b (the current value of b) is 2.

a + b (current values: 1 + 2) is 3.

The tuple (2, 3) is unpacked and assigned:

a = 2

b = 3

At this point:

a = 2

b = 3

c = 3 (unchanged from the initial assignment)

print(a, b, c)

This prints the values of a, b, and c:

Output: 2 3 3

Final Output:

2 3 3

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Python Tuples Quiz

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Day 28 : Python program to print the natural numbers summation pattern

 

def print_summation_pattern(n):

    for i in range(1, n + 1):

        print("+".join([str(x) for x in range(1, i + 1)]))

n = int(input("Enter a number: "))

print_summation_pattern(n)

Code Explanation:

1. Function Definition

def print_summation_pattern(n):

    for i in range(1, n + 1):

        print("+".join([str(x) for x in range(1, i + 1)]))

Purpose:

The function generates and prints a pattern where each line contains a sequence of numbers from 1 to the current line number, separated by +.

Logic Breakdown:

Outer Loop (for i in range(1, n + 1)):

Iterates from 1 to n (inclusive).

Each iteration corresponds to one line in the pattern.

The variable i determines how many numbers will appear on the current line.

Generate Numbers for the Current Line:

[str(x) for x in range(1, i + 1)]

A list comprehension is used to create a list of numbers from 1 to i.

range(1, i + 1) generates numbers from 1 to i (inclusive).

Each number is converted to a string using str(x) for proper formatting with +.

Join Numbers with +:

"+".join([...])

The join method combines the strings in the list, using + as the separator.

Print the Result:

The formatted string is printed, displaying the numbers separated by +.

2. User Input and Function Call

n = int(input("Enter a number: "))

The user is prompted to enter an integer (n).

int(input(...)) converts the input string into an integer.

print_summation_pattern(n)

The print_summation_pattern function is called with n as the argument to generate and print the pattern.

#source code --> clcoding.com 

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Day 27 : Python Program to Print Table of a Given Number

 

def table(number, limit=10):

    print(f"Multiplication table of {number}:")

    for i in range(1, limit + 1):

        print(f"{number} x {i} = {number * i}")

number = int(input("Enter a number: "))

table(number)

Code Explanation:

1. Function Definition

def table(number, limit=10):

    print(f"Multiplication table of {number}:")

Parameters:

number: The number for which the multiplication table is generated.

limit: The maximum value up to which the multiplication table is calculated (default is 10).

Header Message:

The print statement outputs a message indicating the multiplication table being generated.

2. Loop to Generate Multiplication Table

for i in range(1, limit + 1):

    print(f"{number} x {i} = {number * i}")

How It Works:

range(1, limit + 1) generates integers from 1 to limit (inclusive).

The loop iterates through each number i in this range.

In each iteration:

The current value of i is multiplied by number.

The result (number * i) is formatted and printed in the form:

number x i = result

3. User Input and Function Call

number = int(input("Enter a number: "))

table(number)

Input:

The user is prompted to enter an integer (number).

int(input(...)) converts the input string into an integer.

Function Call:

The table function is called with number as the argument. Since the limit parameter has a default value of 10, it does not need to be explicitly specified unless a custom limit is required.

#source code --> clcoding.com

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Day 26 : Python Program to find whether a given number is power of two

 

def power_of_two(n):

    return n > 0 and (n & (n - 1)) == 0

num = int(input("Enter a number: "))

if power_of_two(num):

    print(f"{num} is a power of two.")

else:

    print(f"{num} is not a power of two.")

Code Explanation:

1. Function Definition

def power_of_two(n):

    return n > 0 and (n & (n - 1)) == 0

Parameters and Purpose:

The function power_of_two checks if the input n is a power of two.

Logic Breakdown:

Check if n > 0:

A power of two must be a positive number (e.g., 1, 2, 4, 8, etc.).

If n <= 0, the function immediately returns False.

Bitwise Operation (n & (n - 1)):

This checks if n is a power of two:

A power of two in binary form has exactly one bit set to 1 (e.g., 1 = 0b1, 2 = 0b10, 4 = 0b100).

Subtracting 1 flips all bits from the first set bit to the right:

If the result is 0, the number is a power of two.

2. User Input

num = int(input("Enter a number: "))

The user is prompted to enter an integer (num).

int(input(...)) converts the input string to an integer.

3. Conditional Check and Output

if power_of_two(num):

    print(f"{num} is a power of two.")

else:

    print(f"{num} is not a power of two.")

The power_of_two function is called with num as the argument.

Based on the return value:

If True, the program prints that the number is a power of two.

If False, the program prints that it is not a power of two.

#source code --> clcoding.com

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Python Operators Quiz

 

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Day 20 : Python Program to Find Sum of Digit of a Number using Recursion

 

def sum_of_digits(number):

    if number == 0:

        return 0

    return (number % 10) + sum_of_digits(number // 10)

number = int(input("Enter a number: "))

result = sum_of_digits(abs(number))  

print(f"The sum of the digits of {number} is {result}.")

Code Explanation:

1. Function Definition

def sum_of_digits(number):

def: Declares a new function.

sum_of_digits: The name of the function, which computes the sum of the digits of a number.

number: Input parameter representing the number whose digits will be summed.

2. Base Case for Recursion

    if number == 0:

        return 0

if number == 0:: Checks if the number is 0.

This is the base case for recursion, ensuring the recursion eventually stops.

return 0: Returns 0 if the number is 0. This stops the recursive calls.

3. Recursive Case

    return (number % 10) + sum_of_digits(number // 10)

number % 10: Extracts the last digit of number.

+: Adds the last digit to the result of the recursive call.

4. Taking User Input

number = int(input("Enter a number: "))

input("Enter a number: "): Prompts the user to enter a number.

int(): Converts the input (string) into an integer.

number: Stores the user-provided number.

5. Handling Negative Input

result = sum_of_digits(abs(number))

abs(number): Converts the number to its absolute value (removes any negative sign).

This ensures the function works correctly for negative numbers.

result: Stores the result of the function call.

6. Displaying the Result

print(f"The sum of the digits of {number} is {result}.")

print(f"..."): Displays the sum of the digits in a user-friendly format using an f-string.

The original number and its sum are dynamically inserted into the string.

#source code --> clcoding.com 

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Day 19 : Python Program to Find the Smallest Divisor of an Integer

 

def smallest_divisor(number):

    number = abs(number)  

    if number <= 1:

        return "Input must be greater than 1."

    for i in range(2, number + 1):

        if number % i == 0:  

            return i  

number = int(input("Enter an integer: "))

result = smallest_divisor(number)

print(f"The smallest divisor of {number} is {result}.")

Code Explanation:

1. Function Definition

def smallest_divisor(number):

def: Declares a new function.

smallest_divisor: The name of the function, which finds the smallest divisor of a given number.

number: Input parameter representing the number for which the smallest divisor is to be found.

2. Taking Absolute Value

    number = abs(number)

abs(number): Returns the absolute value of the number (i.e., removes the negative sign if the number is negative).

Example: If number = -10, abs(-10) becomes 10.

This ensures the function works correctly for negative inputs.

3. Handling Small Numbers

    if number <= 1:

        return "Input must be greater than 1."

if number <= 1:: Checks if the input is 1 or less.

The smallest divisor is only meaningful for numbers greater than 1.

return: Ends the function and returns the message "Input must be greater than 1.".

4. Iterating Through Possible Divisors

    for i in range(2, number + 1):

for i in range(2, number + 1):

Loops through potential divisors, starting from 2 (the smallest prime number) up to the number itself.

range(2, number + 1): Generates integers from 2 to number (inclusive).

5. Checking for Divisibility

        if number % i == 0:

if number % i == 0:: Checks if i is a divisor of number by testing if the remainder when dividing number by i is 0.

6. Returning the Smallest Divisor

            return i

return i: Returns the first i that divides the number evenly (i.e., the smallest divisor).

The loop stops as soon as the smallest divisor is found.

7. Taking User Input

number = int(input("Enter an integer: "))

input("Enter an integer: "): Prompts the user to enter a number.

int(): Converts the input from a string to an integer.

number: Stores the user-provided number.

8. Calling the Function and Displaying the Result

result = smallest_divisor(number)

print(f"The smallest divisor of {number} is {result}.")

smallest_divisor(number): Calls the function with the user-provided number and stores the result in result.

print(f"..."): Displays the smallest divisor or the error message in a user-friendly format using an f-string.

#source code --> clcoding.com 

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