Free 40+ Python Books from Amazon – Limited Time Offer!

 

Are you looking for free resources to learn Python? Amazon is offering over 40 Python books for free, including audiobooks that can be instantly accessed with a free Audible trial. This is a great opportunity for beginners and experienced programmers alike to expand their Python skills without spending a dime!

Why Grab These Free Python Books?

• Cost-Free Learning: Save money while gaining valuable knowledge.
• Diverse Topics: Covers beginner to advanced topics, AI-assisted programming, and real-world projects.
• Instant Access: Available instantly in audiobook format with a free trial.

Try Now: Free Books

Top Free Python Books on Amazon Right Now

Here are some of the best free Python books currently available:

Python — The Bible: 3 Manuscripts in 1 Book

• Covers beginner, intermediate, and advanced Python concepts.
• Authors: Maurice J. Thompson, Ronald Hillman.

Python QuickStart Guide

• Ideal for beginners using hands-on projects and real-world applications.
• Authors: Robert Oliver, Andrew Hansen.

Python for Beginners: A Crash Course Guide to Learn Python in 1 Week

• Quick learning resource for those new to Python.
• Authors: Timothy C. Needham, Zac Aleman.

Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT

• Explores AI-powered coding assistance for Python development.
• Authors: Leo Porter, Mark Thomas.

Python Essentials for Dummies

• A beginner-friendly guide from the popular ‘For Dummies’ series.
• Authors: John C. Shovic PhD, Alan Simpson.

How to Get These Books for Free?

1. Visit Amazon and search for the book titles.
2. Select the Audible version (marked as free with a trial).
3. Start your free Audible trial to claim your book.
4. Download and enjoy learning Python!

Final Thoughts

This is a limited-time offer, so grab these books while they are still free! Whether you’re a complete beginner or an experienced coder, these resources will help you level up your Python programming skills.

Happy coding! 🚀

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MACHINE LEARNING WITH PYTHON PROGRAMMING: A Practical Guide to Building Intelligent Applications with Python

 Machine Learning with Python Programming: A Practical Guide to Building Intelligent Applications

Machine Learning (ML) has transformed industries by enabling computers to learn from data and make intelligent decisions. Python has become the go-to programming language for ML due to its simplicity, vast libraries, and strong community support. "Machine Learning with Python Programming: A Practical Guide to Building Intelligent Applications" by Richard D. Crowley is an excellent resource for those looking to develop real-world ML applications using Python.

This book provides a structured and accessible pathway into the world of machine learning.1 Beginning with fundamental concepts and progressing through advanced topics, it covers essential Python libraries, mathematical foundations, and practical applications. The book delves into supervised and unsupervised learning, natural language processing, computer vision, time series analysis, and recommender systems.2 It also addresses critical aspects of model deployment, ethical considerations, and future trends, including reinforcement learning, GANs, and AutoML. With practical examples, troubleshooting tips, and a glossary, this resource empowers readers to build and deploy effective machine learning models while understanding the broader implications of AI.

Why This Book?

This book is designed for beginners and intermediate learners who want to apply ML concepts practically. It provides a hands-on approach to implementing ML algorithms, working with real-world datasets, and deploying intelligent applications.

Some key benefits of reading this book include: 

Step-by-step explanations – Makes it easy to understand complex ML concepts.

Practical coding examples – Helps readers implement ML models in Python.

Covers popular Python libraries – Includes TensorFlow, Scikit-Learn, Pandas, and more.

Real-world use cases – Teaches how to apply ML to solve industry problems.

Key Topics Covered

The book is structured to guide the reader from basic ML concepts to building intelligent applications.

1. Introduction to Machine Learning

Understanding the basics of ML, types of ML (supervised, unsupervised, reinforcement learning), and real-world applications.

Overview of Python as a programming language for ML.

2. Python for Machine Learning

Introduction to essential Python libraries: NumPy, Pandas, Matplotlib, and Scikit-Learn.

Data manipulation and preprocessing techniques.

3. Supervised Learning Algorithms

Implementing regression algorithms (Linear Regression, Polynomial Regression).

Classification algorithms (Logistic Regression, Decision Trees, Support Vector Machines).

4. Unsupervised Learning Techniques

Understanding clustering algorithms (K-Means, Hierarchical Clustering).

Dimensionality reduction with PCA (Principal Component Analysis).

5. Deep Learning with TensorFlow and Keras

Introduction to Neural Networks and Deep Learning.

Building models with TensorFlow and Keras.

Training and optimizing deep learning models.

6. Natural Language Processing (NLP)

Text preprocessing techniques (Tokenization, Lemmatization, Stopword Removal).

Sentiment analysis and text classification using NLP libraries.

7. Real-World Applications of Machine Learning

Building recommender systems for e-commerce.

Fraud detection in financial transactions.

Image recognition and object detection.

8. Deploying Machine Learning Models

Saving and loading ML models.

Using Flask and FastAPI for deploying ML applications.

Integrating ML models into web applications.

Who Should Read This Book?

This book is ideal for: 

 Beginners in Machine Learning – If you're new to ML, this book provides a structured learning path.

Python Developers – If you're comfortable with Python but new to ML, this book will help you get started.

Data Science Enthusiasts – If you want to build practical ML applications, this book is a valuable resource.

Students & Professionals – Whether you're a student or a working professional, this book will enhance your ML skills.

Hard Copy : MACHINE LEARNING WITH PYTHON PROGRAMMING: A Practical Guide to Building Intelligent Applications with Python

Kindle : MACHINE LEARNING WITH PYTHON PROGRAMMING: A Practical Guide to Building Intelligent Applications with Python

Final Thoughts

"Machine Learning with Python Programming: A Practical Guide to Building Intelligent Applications" by Richard D. Crowley is a must-read for anyone looking to dive into ML with Python. It bridges the gap between theory and practice, equipping readers with the necessary skills to build real-world ML solutions.

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Machine Learning System Design: With end-to-end examples

 

Machine Learning System Design: A Deep Dive into End-to-End ML Solutions

Machine Learning (ML) has evolved beyond just algorithms and models; it now requires a robust system design approach to build scalable, reliable, and efficient ML applications. The book "Machine Learning System Design: With End-to-End Examples" by Valerii Babushkin and Arseny Kravchenko is a comprehensive guide for ML practitioners, engineers, and architects who want to design complete ML systems.

From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a reliable framework for building, maintaining, and improving machine learning systems at any scale or complexity.

In Machine Learning System Design: With end-to-end examples you will learn:

• The big picture of machine learning system design

• Analyzing a problem space to identify the optimal ML solution

• Ace ML system design interviews

• Selecting appropriate metrics and evaluation criteria

• Prioritizing tasks at different stages of ML system design

• Solving dataset-related problems with data gathering, error analysis, and feature engineering

• Recognizing common pitfalls in ML system development

• Designing ML systems to be lean, maintainable, and extensible over time

Why Machine Learning System Design Matters

In real-world applications, an ML model is just one component of a larger system. To deploy models effectively, you need to consider various aspects such as:

Data Engineering: Gathering, cleaning, and transforming data for ML.

Feature Engineering: Creating meaningful features to improve model performance.

Model Deployment: Deploying models to production environments with minimal downtime.

Monitoring and Maintenance: Continuously evaluating model performance and updating it when needed.

Scalability & Reliability: Ensuring the system handles large-scale data and requests efficiently.

This book focuses on these critical aspects, making it a valuable resource for those looking to move beyond just training ML models.

Key Topics Covered in the Book

The book is structured to provide both foundational knowledge and practical applications. Some of the key topics include:

1. Fundamentals of ML System Design

Understanding the key components of an ML system.

Trade-offs between accuracy, latency, scalability, and cost.

Common architectures used in production ML systems.

2. Data Management and Processing

Designing robust data pipelines for ML.

Handling real-time vs. batch data processing.

Feature stores and their role in ML workflows.

3. Model Selection and Training Strategies

Choosing the right model for your business problem.

Distributed training techniques for handling large-scale datasets.

Hyperparameter tuning and model optimization strategies.

4. Deployment Strategies

Deploying ML models using different approaches: batch inference, online inference, and edge computing.

A/B testing and canary releases for safe deployments.

Model versioning and rollback strategies.

5. Monitoring, Evaluation, and Maintenance

Setting up monitoring dashboards for model performance.

Detecting data drift and concept drift.

Automating retraining and updating models.

6. Scaling ML Systems

Designing systems that can handle millions of requests per second.

Optimizing for cost and performance.

Distributed computing techniques for ML workloads.

7. Real-World End-to-End Case Studies

Examples of ML system design in domains such as finance, e-commerce, healthcare, and recommendation systems.

Best practices from top tech companies.

Hard Copy : Machine Learning System Design: With end-to-end examples

Kindle : Machine Learning System Design: With end-to-end examples

Who Should Read This Book?

This book is ideal for: 

Machine Learning Engineers – Who want to understand how to take ML models from development to production.

Software Engineers – Who are integrating ML into existing systems.

Data Scientists – Who want to move beyond Jupyter notebooks and understand system-level deployment.

 AI Product Managers – Who need to design ML-powered products and understand technical trade-offs.

Final Thoughts

"Machine Learning System Design: With End-to-End Examples" by Valerii Babushkin and Arseny Kravchenko is a must-read for anyone serious about deploying ML at scale. It goes beyond theory and provides practical insights into how real-world ML systems are built and maintained.

If you're looking to master ML system design and take your ML career to the next level, this book is a great investment in your learning journey.

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Python Coding Challange - Question With Answer(01030325)

 

Step 1: print(0)

This prints 0 to the console.

Step 2: for i in range(1,1):

• The range(start, stop) function generates numbers starting from start (1) and stopping before stop (1).
• The range(1,1) means it starts at 1 but must stop before 1.
• Since the starting value is already at the stopping value, no numbers are generated.

Step 3: print(i) inside the loop

• Since the loop has no numbers to iterate over, the loop body is never executed.
• The print(i) statement inside the loop is never reached.

Final Output:

0

Only 0 is printed, and the loop does nothing.

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Peacock tail pattern using python

 

import numpy as np

import matplotlib.pyplot as plt

n=5000

theta=np.linspace(0,12*np.pi,n)

r=np.linspace(0,1,n)+0.2*np.sin(6*theta)

x=r*np.cos(theta)

y=r*np.sin(theta)

colors=np.linspace(0,1,n)

plt.figure(figsize=(8,8))

plt.scatter(x,y,c=colors,cmap='viridis',s=2,alpha=0.8)

plt.axis('off')

plt.title('Peacock tail pattern',fontsize=14,fontweight='bold',color='darkblue')

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Importing Required Libraries

import numpy as np

import matplotlib.pyplot as plt

numpy is used for numerical operations such as generating arrays and applying mathematical functions.

matplotlib.pyplot is used for visualization, specifically for plotting the peacock tail pattern.

2. Defining the Number of Points

n = 5000  

Sets the number of points to 5000, meaning the pattern will be composed of 5000 points.

A higher n creates a smoother and more detailed pattern.

3. Generating Angular and Radial Coordinates

theta = np.linspace(0, 12 * np.pi, n)

Creates an array of n values from 0 to 12π (i.e., multiple full circular rotations).

The linspace() function ensures a smooth transition of angles, creating a spiral effect.

r = np.linspace(0, 1, n) + 0.2 * np.sin(6 * theta)  

np.linspace(0, 1, n): Generates a gradual outward movement from the center.

0.2 * np.sin(6 * theta): Adds a wave-like variation to the radial distance, creating feather-like oscillations.

4. Converting Polar Coordinates to Cartesian Coordinates

x = r * np.cos(theta)

y = r * np.sin(theta)

Converts the polar coordinates (r, θ) into Cartesian coordinates (x, y), which are required for plotting in Matplotlib.

The transformation uses:

x = r * cos(θ) → Determines the horizontal position.

y = r * sin(θ) → Determines the vertical position.

5. Assigning Colors to Points

colors = np.linspace(0, 1, n)

Generates a gradient of values from 0 to 1, which will later be mapped to a colormap (viridis).

This helps create a smooth color transition in the final pattern.

6. Creating the Plot

plt.figure(figsize=(8, 8))

Creates a figure with a square aspect ratio (8x8 inches) to ensure the spiral appears circular and not stretched.

plt.scatter(x, y, c=colors, cmap='viridis', s=2, alpha=0.8)  

Uses scatter() to plot the generated (x, y) points.

c=colors: Colors the points using the gradient values generated earlier.

cmap='viridis': Uses the Viridis colormap, which transitions smoothly from dark blue to bright yellow.

s=2: Sets the size of each point to 2 pixels for fine details.

alpha=0.8: Makes points slightly transparent to enhance the blending effect.

7. Formatting the Plot

plt.axis('off')

Removes axes for a clean and aesthetic visualization.

plt.title("Peacock Tail Pattern", fontsize=14, fontweight='bold', color='darkblue')

Adds a title to the plot with:

fontsize=14: Medium-sized text.

fontweight='bold': Bold text.

color='darkblue': Dark blue text color.

8. Displaying the Plot

plt.show()

Displays the generated Peacock Tail Pattern.

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Fish Scale pattern plot using python

 import numpy as np

import matplotlib.pyplot as plt

rows,cols=10,10

radius=1

fig,ax=plt.subplots(figsize=(8,8))

ax.set_xlim(0,cols*radius)

ax.set_ylim(0,(rows+0.5)*(radius/2))

ax.set_aspect('equal')

ax.axis('off')

color=['#6FA3EF','#3D7A9E','#F4C542','#E96A64','#9C5E75']

for row in range(rows):

    for col in range(cols):

        x=col*radius

        y=row*(radius/2)

        if row%2==1:

            x+=radius/2

        semicircle=plt.cCircle((x,y),radius,color=np.random.choice(colors),clip_on=False)

        ax.add_patch(semicircle)

plt.title('Fish scale pattern plot',fontsize=16,fontweight='bold',color='navy',pad=15)

plt.show()

#source code --> clcoding.com       

Code Explanation:

Import required libraries

import numpy as np

import matplotlib.pyplot as plt

Imports numpy (as np) and matplotlib.pyplot (as plt)

numpy is used for mathematical operations and randomness

matplotlib.pyplot is used for creating plots

Setting Grid Size & Circle Radius:

rows, cols = 10, 10  

radius = 1  

Defines the number of rows and columns (10×10 grid)

Defines the radius of each semicircle as 1

Creating the Figure & Axes:

fig, ax = plt.subplots(figsize=(8,8))

Creates a figure (fig) and an axis (ax) with an 8×8-inch canvas

plt.subplots() is used for making a figure with subplots

Setting Axis Limits:

ax.set_xlim(0, cols * radius)

ax.set_ylim(0, (rows + 0.5) * (radius / 2))  

ax.set_xlim(0, cols * radius) → X-axis ranges from 0 to cols * radius

ax.set_ylim(0, (rows + 0.5) * (radius / 2)) → Y-axis height is adjusted for proper alignment of semicircles

(rows + 0.5) * (radius / 2) ensures the last row is properly visible

Making the Plot Circular and Removing Axes:

ax.set_aspect('equal') 

ax.axis('off')

ax.set_aspect('equal') → Maintains equal scaling for X and Y axes, ensuring circles remain perfectly round

ax.axis('off') → Removes the axes, labels, and ticks for a clean look

Defining Colors:

colors = ['#6FA3EF', '#3D7A9E', '#F4C542', '#E96A64', '#9C5E7F']

Creates a list of five colors (in hex format)

These colors will be randomly assigned to the semicircles

Looping to Create Fish Scales:

for row in range(rows):

    for col in range(cols):

Outer loop (row) iterates through each row

Inner loop (col) iterates through each column

Calculating X & Y Positions:

x = col * radius  

y = row * (radius / 2)  

x = col * radius → Sets the X position for each semicircle

y = row * (radius / 2) → Sets the Y position for stacking semicircles half overlapping

Offsetting Alternate Rows:

if row % 2 == 1:

    x += radius / 2  

If row is odd, shift X position by radius / 2

This staggered alignment mimics the overlapping scale effect

Drawing the Semicircles:

semicircle = plt.Circle((x, y), radius, color=np.random.choice(colors), clip_on=False)

ax.add_patch(semicircle)

plt.Circle((x, y), radius, color=np.random.choice(colors), clip_on=False)

Creates a circle at (x, y) with radius = 1

Fills it with a random color from the color list (np.random.choice(colors))

clip_on=False ensures the circles aren't clipped at the edges

ax.add_patch(semicircle) → Adds the semicircle to the plot

Adding a Title:

plt.title("Fish Scale Pattern", fontsize=16, fontweight='bold', color='navy', pad=15)

Adds a title: "Fish Scale Pattern"

Font settings:

fontsize=16 → Large text

fontweight='bold' → Bold font

color='navy' → Dark blue text

pad=15 → Adds extra spacing above the title

Displaying the Pattern:

plt.show()

Displays the final fish scale pattern plot

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Fractal tree pattern plot using python

 

import matplotlib.pyplot as plt

import numpy as np

def draw_branch(ax, x, y, length, angle, depth, branch_factor=0.7, angle_variation=30):

    if depth == 0:

        return

    new_x = x + length * np.cos(np.radians(angle))

    new_y = y + length * np.sin(np.radians(angle))

    ax.plot([x, new_x], [y, new_y], 'brown', lw=depth)

    draw_branch(ax, new_x, new_y, length * branch_factor, angle + angle_variation, depth - 1)

    draw_branch(ax, new_x, new_y, length * branch_factor, angle - angle_variation, depth - 1)

def fractal_tree():

    fig, ax = plt.subplots(figsize=(8, 8))

    ax.set_xticks([])

    ax.set_yticks([])

    ax.set_frame_on(False)

    draw_branch(ax, 0, -1, 1, 90, depth=10)

    plt.xlim(-1, 1)

    plt.ylim(-1, 1.5)

    plt.title('Fractal tree pattern plot')

    plt.show()

fractal_tree()

#source code --> clcoding.com 

Code Explanation:

1. Importing Required Libraries

import matplotlib.pyplot as plt

import numpy as np

matplotlib.pyplot: Used to plot the fractal tree.

numpy: Used for mathematical operations like cos and sin (for angle calculations).

2. Recursive Function to Draw Branches

def draw_branch(ax, x, y, length, angle, depth, branch_factor=0.7, angle_variation=30):

This function draws branches recursively.

Parameters:

ax: The matplotlib axis for plotting.

(x, y): The starting coordinates of the branch.

length: The length of the current branch.

angle: The angle at which the branch grows.

depth: The recursion depth, representing how many branch levels to draw.

branch_factor: Determines how much shorter each successive branch is.

angle_variation: Determines how much the new branches deviate from the main branch.

3. Base Condition (Stopping Recursion)

if depth == 0:

    return

When depth == 0, recursion stops, meaning the smallest branches are reached.

4. Calculating the New Branch Endpoints

new_x = x + length * np.cos(np.radians(angle))

new_y = y + length * np.sin(np.radians(angle))

Uses trigonometry to compute the (x, y) coordinates of the new branch:

cos(angle): Determines the horizontal displacement.

sin(angle): Determines the vertical displacement.

np.radians(angle): Converts degrees to radians.

5. Drawing the Branch

ax.plot([x, new_x], [y, new_y], 'brown', lw=depth)

The branch is drawn as a brown line.

lw=depth: Line width decreases as depth decreases (thicker at the bottom, thinner at the tips).

6. Recursively Creating Two New Branches

draw_branch(ax, new_x, new_y, length * branch_factor, angle + angle_variation, depth - 1)

draw_branch(ax, new_x, new_y, length * branch_factor, angle - angle_variation, depth - 1)

Two smaller branches sprout from the current branch at:

angle + angle_variation (left branch).

angle - angle_variation (right branch).

Each new branch has:

A reduced length (length * branch_factor).

One less depth (depth - 1).

7. Creating the Fractal Tree

def fractal_tree():

    fig, ax = plt.subplots(figsize=(8, 8))

    ax.set_xticks([])

    ax.set_yticks([])

    ax.set_frame_on(False)

Creates a figure and axis for plotting.

Removes ticks (set_xticks([]), set_yticks([])) and frame (set_frame_on(False)) for a clean look.

8. Calling the Recursive Function

draw_branch(ax, 0, -1, 1, 90, depth=10)

Starts drawing the tree at (0, -1), which is near the bottom center.

The initial branch:

Length = 1

Angle = 90° (straight up)

Depth = 10 (controls the number of branch levels).

9. Setting Plot Limits & Displaying the Tree

plt.xlim(-1, 1)

plt.ylim(-1, 1.5)

plt.title('Fractal tree pattern plot')

plt.show()

Limits the x-axis (-1 to 1) and y-axis (-1 to 1.5) to keep the tree centered.

Displays the fractal tree with plt.show().

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