Galaxy Swirl Pattern using Python

 

import numpy as np

import matplotlib.pyplot as plt

num_stars=1000

theta=np.linspace(0,4*np.pi,num_stars)

r=np.exp(0.3*theta)

noise=np.random.normal(scale=0.2,size=(num_stars,2))

x=r*np.cos(theta)+noise[:,0]

y=r*np.sin(theta)+noise[:,-1]

plt.figure(figsize=(6,6),facecolor="black")

plt.scatter(x,y,color="white",s=2)

plt.scatter(-x,-y,color="white",s=2)

plt.axis('off')

plt.title("Galaxy swirl pattern",fontsize=14,color="white")

plt.show()

#source code --> clcoding.com

Code Explanation

Step 1: Import Required Libraries

import numpy as np

import matplotlib.pyplot as plt

numpy (np): Used for numerical computations, like creating arrays, generating random values, and performing trigonometric calculations.

matplotlib.pyplot (plt): Used for creating and customizing the visualization.

Step 2: Define the Number of Stars

num_stars = 1000  

This sets the total number of stars that will be plotted in the galaxy.

A larger number (e.g., 5000) would create a denser galaxy.

Step 3: Generate Spiral Angle (θ)

theta = np.linspace(0, 4 * np.pi, num_stars)  

theta represents the angle in radians.

np.linspace(0, 4 * np.pi, num_stars) generates 1000 values from 0 to 4π (two full turns).

This controls the swirl or spiral effect in the galaxy.

Step 4: Compute Spiral Radius (r)

r = np.exp(0.3 * theta)  

This is the equation of a logarithmic spiral:

r=e 0.3θ

 exp(0.3 * theta) ensures that the spiral expands outward as theta increases.

The factor 0.3 controls the tightness of the swirl:

Lower values (e.g., 0.2) → Tighter spiral.

Higher values (e.g., 0.5) → More open swirl.

Step 5: Introduce Random Noise (Star Distribution)

noise = np.random.normal(scale=0.2, size=(num_stars, 2))

This adds randomness to the star positions to make them look more natural.

np.random.normal(scale=0.2, size=(num_stars, 2)):

Generates Gaussian noise with a mean of 0 and a standard deviation of 0.2.

The shape (num_stars, 2) means each star gets a random X and Y offset.

This makes the stars appear slightly scattered instead of forming a perfect spiral.

Step 6: Convert Polar Coordinates to Cartesian Coordinates

x = r * np.cos(theta) + noise[:, 0]

y = r * np.sin(theta) + noise[:, 1]

Converts the polar coordinates (r, theta) into Cartesian coordinates (x, y).

Equations:

x=r⋅cos(θ)+random noise

y=r⋅sin(θ)+random noise

The random noise slightly shifts each star away from the perfect spiral, creating a realistic galaxy.

Step 7: Set Up the Plot with a Black Background

plt.figure(figsize=(8, 8), facecolor="black")

Creates a new figure with a square aspect ratio (8×8 inches).

facecolor="black" sets the background color to black for a space-like appearance.

Step 8: Plot the Stars (White Dots)

plt.scatter(x, y, color="white", s=2)  

plt.scatter(x, y, color="white", s=2):

Plots each star as a tiny white dot.

s=2: Controls the size of each star (can be increased for a brighter look).

Step 9: Create a Symmetric Swirl

plt.scatter(-x, -y, color="white", s=2)

This mirrors the swirl in the opposite direction.

Instead of just x, y, we plot -x, -y to create a symmetric pattern, making the galaxy more balanced.

Step 10: Hide the Axes for a Clean Look

plt.axis("off")

Removes the X and Y axes, making the visualization look more like deep space.

Step 11: Add a Title

plt.title("Galaxy Swirl Pattern", fontsize=14, color="white")

Adds a title "Galaxy Swirl Pattern" in white color.

Step 12: Display the Final Plot

plt.show()

Renders the final galaxy swirl on the screen.

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Rose Curve Pattern using python

 

import numpy as np

import matplotlib.pyplot as plt

a=5

k=7

theta=np.linspace(0,2*np.pi,1000)

r=a*np.cos(k*theta)

x=r*np.cos(theta)

y=r*np.sin(theta)

plt.figure(figsize=(6,6),facecolor="black")

plt.plot(x,y,color='magenta',linewidth=2)

plt.axis('off')

plt.title(f"Rose curve pattern(k={k})",color="white",fontsize=14)

plt.show()

#source code --> clcoding.com

Code Explanation: 

Step 1: Import Required Libraries

import numpy as np

import matplotlib.pyplot as plt

numpy (np): Used for numerical computations (e.g., generating angle values for the curve).

matplotlib.pyplot (plt): Used for plotting the Rose Curve.

Step 2: Define Parameters for the Rose Curve

a = 5  

k = 7  

a (Amplitude): Controls the size of the rose curve.

k (Frequency): Defines the number of petals in the Rose Curve.

If k is odd, the Rose Curve has k petals.

If k is even, the Rose Curve has 2k petals.

Step 3: Generate Theta Values (Angle in Radians)

theta = np.linspace(0, 2 * np.pi, 1000)

theta represents the angle in radians, ranging from 0 to 2π (a full circle).

np.linspace(0, 2 * np.pi, 1000) generates 1000 equally spaced values between 0 and 2π, ensuring a smooth curve.

Step 4: Compute r (Polar Radius) for the Rose Curve

r = a * np.cos(k * theta)

This is the polar equation of the Rose Curve:

r=a⋅cos(kθ)

a (5) scales the curve, affecting the size.

k * theta determines the oscillation frequency of the petals.

Step 5: Convert Polar Coordinates to Cartesian Coordinates

x = r * np.cos(theta)

y = r * np.sin(theta)

Since matplotlib plots in Cartesian coordinates (x, y), we convert from polar to Cartesian:

x=r⋅cos(θ)

y=r⋅sin(θ)

This transforms the radius (r) and angle (theta) into X, Y coordinates.

Step 6: Set Up the Figure for Plotting

plt.figure(figsize=(6,6), facecolor="black")

figsize=(6,6): Creates a square figure (6x6 inches).

facecolor="black": Sets the background color to black for a stylish effect.

Step 7: Plot the Rose Curve

plt.plot(x, y, color='magenta', linewidth=2)

plt.plot(x, y, color='magenta', linewidth=2)

x, y: The calculated coordinates of the Rose Curve.

color='magenta': Sets the line color to magenta (pinkish-purple).

linewidth=2: Controls the thickness of the curve.

Step 8: Hide Axes for a Clean Look

plt.axis("off")

Hides the X and Y axes to make the design look more artistic.

Step 9: Add a Title

plt.title(f"Rose Curve (k={k})", color="white", fontsize=14)

Displays a title:

Uses f"Rose Curve (k={k})" to dynamically show the value of k in the title.

color="white" makes the title white for visibility on a black background.

fontsize=14 sets the font size.

Step 10: Display the Plot

plt.show()

Displays the final Rose Curve plot.

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Star Constellation Pattern using Python

 

import numpy as np

import matplotlib.pyplot as plt

num_stars = 30  

x_vals = np.random.uniform(0, 10, num_stars)

y_vals = np.random.uniform(0, 10, num_stars)

sizes = np.random.randint(20, 100, num_stars)

num_connections = 10

connections = np.random.choice(num_stars, (num_connections, 2), replace=False)

fig, ax = plt.subplots(figsize=(8, 8), facecolor='black')

ax.set_facecolor("black")

ax.scatter(x_vals, y_vals, s=sizes, color='white', alpha=0.8)

for start, end in connections:

    ax.plot([x_vals[start], x_vals[end]], [y_vals[start], y_vals[end]], 

            color='white', linestyle='-', linewidth=0.8, alpha=0.6)

ax.set_xticks([])

ax.set_yticks([])

ax.set_frame_on(False)

plt.title("Star Constellation Pattern", fontsize=14, fontweight="bold", color="white")

plt.show()

#source code --> clcoding.com

Code Explanation:

Import Required Libraries

import numpy as np

import matplotlib.pyplot as plt

numpy: Used for generating random numbers (star positions, sizes, and connections).

matplotlib.pyplot: Used for creating the star constellation plot.

Define Number of Stars

num_stars = 30  

Specifies the total number of stars in the pattern.

Generate Random Star Positions

x_vals = np.random.uniform(0, 10, num_stars)

y_vals = np.random.uniform(0, 10, num_stars)

Generates num_stars random x and y coordinates.

np.random.uniform(0, 10, num_stars): Creates values between 0 and 10 to place stars randomly in a 10x10 grid.

Assign Random Star Sizes

sizes = np.random.randint(20, 100, num_stars)

Assigns a random size to each star.

np.random.randint(20, 100, num_stars): Generates star sizes between 20 and 100, making some stars appear brighter than others.

Create Random Connections Between Stars

num_connections = 10

connections = np.random.choice(num_stars, (num_connections, 2), replace=False)

num_connections = 10: Specifies that 10 pairs of stars will be connected with lines.

np.random.choice(num_stars, (num_connections, 2), replace=False):

Randomly selects 10 pairs of star indices (from 0 to num_stars-1).

Ensures that each pair is unique (replace=False).

These pairs form the constellation-like connections.

Create the Figure and Set Background

fig, ax = plt.subplots(figsize=(6, 6), facecolor='black')

ax.set_facecolor("black")

plt.subplots(figsize=(6,6)): Creates a 6x6 inches figure.

facecolor='black': Sets the entire figure background to black.

ax.set_facecolor("black"): Ensures the plot area (inside the figure) is also black.

Plot Stars as White Dots

ax.scatter(x_vals, y_vals, s=sizes, color='white', alpha=0.8)

Plots stars using scatter():

x_vals, y_vals: Star positions.

s=sizes: Varying sizes of stars.

color='white': Stars are white.

alpha=0.8: Adds slight transparency to blend the stars naturally.

Draw Lines to Connect Some Stars (Constellation Effect)

for start, end in connections:

    ax.plot([x_vals[start], x_vals[end]], [y_vals[start], y_vals[end]], 

            color='white', linestyle='-', linewidth=0.8, alpha=0.6)

Loops through each selected star pair (start, end).

Uses ax.plot() to draw a faint white line between the two stars.

color='white': Makes lines visible on a black background.

linestyle='-': Uses solid lines.

linewidth=0.8: Thin lines for a delicate look.

alpha=0.6: Faint transparency to make lines blend smoothly.

Remove Axis Labels for a Clean Look

ax.set_xticks([])

ax.set_yticks([])

ax.set_frame_on(False)

Removes x and y ticks (ax.set_xticks([]), ax.set_yticks([])) so no numerical labels are shown.

Removes plot frame (ax.set_frame_on(False)) to give a seamless night-sky effect.

Add a Title and Display the Plot

plt.title("Star Constellation Pattern", fontsize=14, fontweight="bold", color="white")

plt.show()

Adds a title "Star Constellation Pattern" in white.

plt.show() displays the final constellation pattern.

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Python Coding Challange - Question With Answer(01170325)

 

Execution Flow:

1. The first if(True) condition is True, so the code inside it runs.
   • It prints "0".
2. Inside the first if block, there's another if(True), which is also True.
   • It prints "1".
3. The else block is skipped because the first if condition was True.

Output:

0
1

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Python Coding challenge - Day 407| What is the output of the following Python Code?

 

Step-by-Step Code Explanation

Imports necessary libraries:

import statsmodels.api as sm  

import numpy as np  

statsmodels.api is used for statistical modeling.

numpy is used for numerical computations and array handling.

x = np.array([1, 2, 3, 4, 5])  

y = np.array([2, 4, 6, 8, 10])  

Defines input variables:

x = [1, 2, 3, 4, 5] (independent variable)

y = [2, 4, 6, 8, 10] (dependent variable)

The relationship is perfectly linear with y = 2x.

model = sm.OLS(y, sm.add_constant(x)).fit()  

Creates and fits an Ordinary Least Squares (OLS) regression model:

sm.add_constant(x) adds a bias (intercept) term to x.

sm.OLS(y, X).fit() computes the best-fit line for y = 2x.

print(model.params[1])

Extracts and prints the slope (coefficient) of x

The slope represents how much y changes for each unit change in x.

Ideally, the output should be 2.0, but the actual printed value is:

1.9999999999999998

Why is the Output 1.9999999999999998 Instead of 2.0?

This happens due to floating-point precision errors in Python. Here’s why:

Floating-Point Representation

Computers store decimal numbers in binary floating-point format, which sometimes cannot exactly represent simple decimals.

Tiny rounding errors occur during calculations, leading to results like 1.9999999999999998 instead of 2.0.

Final Answer (Exact Value with Floating-Point Precision)

1.999

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Python Coding challenge - Day 406| What is the output of the following Python Code?

 

Code Explanation:

Importing NetworkX

import networkx as nx  

Imports the networkx library, which is used for graph creation and analysis in Python.

Creating a Directed Graph

G = nx.DiGraph([(1, 2), (2, 3), (3, 1), (3, 4)])  

Creates a directed graph (DiGraph).

Adds four directed edges:

1 → 2

2 → 3

3 → 1

3 → 4

Graph Representation:

   1 → 2  

   ↑    ↓  

   3 ←  4  

Finding Predecessors of Node 3

pred = list(G.predecessors(3))  

Finds all nodes that have an edge pointing to 3 (predecessors).

In the graph:

2 → 3 (So 2 is a predecessor)

1 → 3 (So 1 is a predecessor)

Output:

[2, 1]

Finding Successors of Node 3

succ = list(G.successors(3))  

Finds all nodes where 3 points to (successors).

In the graph:

3 → 1

3 → 4

Output:

[4]

Printing Predecessors and Successors

print(pred, succ)  

Prints the predecessors and successors of node 3.

Final Output:

[2] [1,4]

[][2] [1, 4][2] [1, 4]

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Python Coding challenge - Day 405| What is the output of the following Python Code?

 

Code Explanation:

Import the Required Module

import torch.nn as nn

This imports PyTorch’s neural network module, which contains various activation functions and layers.

Create a ReLU Activation Function

relu = nn.ReLU()

This initializes an instance of the ReLU (Rectified Linear Unit) function.

Apply ReLU to a Tensor

print(relu(torch.tensor([-2.0, 0.0, 3.0])))

torch.tensor([-2.0, 0.0, 3.0]) creates a PyTorch tensor with three values: -2.0, 0.0, 3.0.

relu() applies the ReLU activation function to each element of the tensor.

Understanding ReLU:

The ReLU function is defined as:

ReLU(x)=max(0,x)

Which means:

If the input value is negative, ReLU outputs 0.

If the input value is 0 or positive, ReLU outputs the same value.

Expected Output:

For the given tensor [-2.0, 0.0, 3.0]:

ReLU(-2.0) = max(0, -2.0) = 0.0

ReLU(0.0) = max(0, 0.0) = 0.0

ReLU(3.0) = max(0, 3.0) = 3.0

So the printed output will be:

tensor([0., 0., 3.])

Final Output:

tensor([0., 0., 3.])

[0.0, 0.0, 3.0][0.0, 0.0, 3.0]

[0.0,[0.0, 0.0, 3.00.0, 3.0][0.0, 0.0, 3.0]

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Python Coding Challange - Question With Answer(01150325)

 

Step-by-Step Execution:

height = 185
1. if not (height == 170):
   • height == 170 is False because 185 != 170
   • not False → True
   • So, print("1") executes.
2. if not (height > 160 and height < 200):
   • height > 160 is True
   • height < 200 is True
   • True and True is True
   • not True → False
   • So, print("2") does not execute.

Final Output:

1

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Python Coding challenge - Day 404| What is the output of the following Python Code?

 

Code Explanation:

Step 1: Import NumPy

import numpy as np

numpy is a library for numerical computing in Python.

Step 2: Define Matrices 

A and 𝐵

A = np.array([[1, 2], [3, 4]])

B = np.array([[5, 6], [7, 8]])

A and B are 2×2 matrices:

A=[ 13 24]

B=[ 57 68]

Step 3: Compute the Matrix Multiplication

result = np.dot(A, B)

The np.dot(A, B) function computes the dot product (matrix multiplication for 2D arrays).

Matrix Multiplication Formula:

C=A⋅B

Step 4: Print the Result

print(result)

Final Output:

[[19 22]

 [43 50]]

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Python Coding challenge - Day 403| What is the output of the following Python Code?

 

Code Explanation:

Step 1: Importing the pipeline from transformers

from transformers import pipeline

The pipeline function from the transformers library simplifies access to various pre-trained models.

Here, it's used for sentiment analysis.

Step 2: Creating a Sentiment Analysis Pipeline

classifier = pipeline("sentiment-analysis")

This automatically loads a pre-trained model for sentiment analysis.

By default, it uses distilbert-base-uncased-finetuned-sst-2-english, a model trained on the Stanford Sentiment Treebank (SST-2) dataset.

The model classifies text into positive or negative sentiment.

Step 3: Running Sentiment Analysis on a Given Text

result = classifier("I love AI and Python!")

The classifier analyzes the input "I love AI and Python!" and returns a list of dictionaries containing:

label: The predicted sentiment (POSITIVE or NEGATIVE).

score: The confidence score of the prediction (between 0 and 1).

Example output:

[{'label': 'POSITIVE', 'score': 0.9998}]

Here, the model predicts POSITIVE sentiment with a high confidence score.

Step 4: Extracting and Printing the Sentiment Label

print(result[0]['label'])

Since result is a list with a single dictionary, result[0] gives:

{'label': 'POSITIVE', 'score': 0.9998}

result[0]['label'] extracts the sentiment label, which is "POSITIVE".

Final Output:

POSITIVE

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Python Coding challenge - Day 402| What is the output of the following Python Code?

Code Explanation:

Step 1: Create a Tensor

x = torch.tensor([1.0, 2.0, 3.0])

This creates a 1D tensor:

x=[1.0,2.0,3.0]

Step 2: Element-wise Subtraction

x - 2

Each element in the tensor is subtracted by 2:

[1.0−2,2.0−2,3.0−2]=[−1.0,0.0,1.0]

Step 3: Apply ReLU (Rectified Linear Unit)

y = torch.relu(x - 2)

The ReLU function is defined as:

Applying ReLU to [-1.0, 0.0, 1.0]:

ReLU(-1.0) = 0.0

ReLU(0.0) = 0.0

ReLU(1.0) = 1.0

Thus, the result is:

y=[0.0,0.0,1.0]

Final Output:

tensor([0., 0., 1.])

 

Read More

Happy Pi Day using Python

 

import numpy as np, matplotlib.pyplot as plt, sympy as sp, random

print(f"🎉 Happy Pi Day! 🎉\nπ ≈ 3.{str(sp.N(sp.pi, 12))[2:]}")

def monte_carlo_pi(n=5000):

    inside = sum(1 for _ in range(n) if (x:=random.random())**2 

                 + (y:=random.random())**2 <= 1)

    plt.scatter(np.random.rand(n), np.random.rand(n), 

                c=['blue' if x**2 + y**2 <= 1 else 'red' for x, y 

                   in zip(np.random.rand(n), np.random.rand(n))], s=1)

    plt.title(f"Monte Carlo π ≈ {4 * inside / n:.5f}"), plt.show()

monte_carlo_pi()  

#source code --> clcoding.com

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Python Coding Challange - Question With Answer(01130325)

 

Step-by-Step Execution:

1. Initialization:

   number = 0

2. First Iteration (Loop Start - number = 0):

   • number += 4 → number = 4
   • if number == 7: → False, so continue does not execute.
   • print(4)

3. Second Iteration (Loop Start - number = 4):

   • number += 4 → number = 8
   • if number == 7: → False, so continue does not execute.
   print(8)

4. Third Iteration (Loop Start - number = 8):

   • Condition while number < 8: fails because number is no longer less than 8.
   • The loop stops.

---

Final Output:

4
8

---

Understanding the continue Statement:

• The continue statement skips the rest of the current iteration and moves to the next loop cycle.
• However, number == 7 never occurs in this program because the values of number are 4 and 8.
• So, the continue statement never runs and has no effect in this case.

Key Observations:

1. The loop increments number by 4 in each iteration.
2. The condition number == 7 never happens.
3. The loop stops when number reaches 8.

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Python Coding challenge - Day 401| What is the output of the following Python Code?

 

Code Explanation:

Step 1: Import PyTorch

import torch

This imports the PyTorch library, which is used for deep learning, tensor computations, and GPU acceleration.

torch provides various tensor operations similar to NumPy but optimized for deep learning.

Step 2: Create a Tensor

x = torch.tensor([1.0, 2.0, 3.0])

This creates a 1D tensor with floating-point values [1.0, 2.0, 3.0].

torch.tensor([...]) is used to initialize a tensor.

The values are explicitly written as floating-point (1.0, 2.0, 3.0) because PyTorch defaults to float32 if no data type is specified.

Step 3: Apply ReLU Activation

y = torch.relu(x - 2)

First, the expression x - 2 is computed:

x - 2  # Element-wise subtraction

This means:

[1.0, 2.0, 3.0] - 2

= [-1.0, 0.0, 1.0]

Next, torch.relu() is applied:

torch.relu() is the Rectified Linear Unit (ReLU) activation function, which is widely used in neural networks.

It is defined as:

𝑅

𝑒

eLU(x)=max(0,x)

Applying ReLU to [-1.0, 0.0, 1.0]:

lua

Copy

Edit

ReLU(-1.0) = max(0, -1.0) = 0.0

ReLU(0.0) = max(0, 0.0) = 0.0

ReLU(1.0) = max(0, 1.0) = 1.0

Final result for y:

y = [0.0, 0.0, 1.0]

Step 4: Print Output

print(y)

This prints the final tensor y after applying ReLU:

tensor([0., 0., 1.])

This means:

-1.0 became 0.0

0.0 remained 0.0

1.0 remained 1.0

Final Output

tensor([0., 0., 1.])

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Python Coding challenge - Day 400| What is the output of the following Python Code?

 

Code Explanation:

from scipy.optimize import bisect

This imports the bisect function from the scipy.optimize module.

The bisection method is a numerical approach to finding roots of a function.

def f(x):

    return x**3 - 7*x + 6

This defines a function f(x), which takes an input x and returns the value of the polynomial:

f(x)=x 3−7x+6

This function will be used to find its root.

root = bisect(f, 0, 2)

The bisect() function is called to find a root of f(x) within the interval [0, 2].

The function bisect(f, a, b) works as follows:

It checks if f(a) and f(b) have opposite signs (i.e., one is positive and one is negative).

If yes, it repeatedly halves the interval until it finds the root or an approximation.

If f(a) or f(b) is exactly zero, that value is returned as the root.

Checking our function in the interval [0, 2]:

f(0)=0 3−7(0)+6=6 (positive)

f(2)=2 3−7(2)+6=8−14+6=0 (zero)

Since 

f(2)=0, the root is exactly 2.

print(round(root, 4))

This prints the computed root, rounded to 4 decimal places.

However, since the root is exactly 2, the output will simply be:

2.0

Final Output:

2.0

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Python Coding Challange - Question With Answer(01120325)

 

Let's analyze the code step by step:

python
val = 9  # Step 1: Assigns the value 9 to the variable 'val'
val = 10 # Step 2: Reassigns the value 10 to the variable 'val'
print(val) # Step 3: Prints the current value of 'val'

Explanation:

1. First assignment (val = 9): The variable val is created and assigned the value 9.

2. Second assignment (val = 10): The value 9 is overwritten by 10. In Python, variables store only one value at a time, so the previous value (9) is lost.

3. Printing the value (print(val)): Since val now holds 10, the output will be:

   10

Key Takeaways:

• Python executes statements sequentially.
• The latest assignment determines the final value of a variable.
• The previous value (9) is replaced and no longer accessible.

Thus, the output of the given code is 10.

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AI For Beginners: Grasp Generative AI and Machine Learning, Advance Your Career, and Explore the Ethical Implications of Artificial Intelligence in Just 31 Days

 

Artificial Intelligence is everywhere—powering your Netflix recommendations, optimizing your online shopping, and even shaping the future of work. But with all the buzz, it’s easy to feel overwhelmed. 

You don’t need a technical background to understand AI. This book is designed for complete beginners whether you're a student, a professional exploring new opportunities, or just curious about how AI works. Everything is explained in a clear, simple way, with hands-on exercises to help you learn by doing.

Artificial Intelligence (AI) is transforming industries and redefining the future of work. For those eager to understand and harness its potential, the book "Essentials of AI for Beginners: Unlock the Power of Machine Learning, Generative AI & ChatGPT to Advance Your Career, Boost Creativity & Keep Pace with Modern Innovations even if you’re not Tech-Savvy" serves as a comprehensive guide. 

This 31-day beginner’s guide takes you on an interactive learning journey—step by step—breaking down Generative AI, Machine Learning, and real-world applications in a way that actually makes sense.

Inside, you’ll discover:

•  AI Fundamentals—Key concepts, from algorithms to neural networks, explained simply.
•  Hands-On Projects—Build your own chatbot, recommender system, and AI-driven music.
•  Python for AI—Learn essential Python skills with easy-to-follow exercises (even if you've never coded before!).
•  AI in Everyday Life—How AI is shaping finance, healthcare, entertainment, and more.
•  Career Boosting Insights—Discover AI-powered job opportunities and how to transition into the field.
•  Ethical AI Considerations—Privacy, bias, and the big questions AI raises about our future.

Book Overview

 This book demystifies AI concepts, making them accessible to readers without a technical background. It covers fundamental topics such as machine learning, deep learning, generative AI, and ChatGPT, providing readers with a solid foundation in AI technologies. 

Key Features

In-Depth Yet Accessible Content: The book offers a thorough exploration of AI while remaining beginner-friendly, ensuring readers can grasp complex topics without feeling overwhelmed. 

Hands-On Learning: It includes step-by-step tutorials and activities, allowing readers to apply AI concepts practically and progress from novice to proficient. 

Real-World Applications: Through relatable analogies and case studies, the book demonstrates how AI is transforming industries like healthcare, finance, education, and entertainment. 

Creativity Enhancement: Readers discover AI tools for writing, gaming, music composition, art, and content creation, showcasing AI's role in boosting creativity. 

Ethical Considerations: The book addresses AI ethics, discussing its impact on privacy, bias, and societal implications, ensuring readers are aware of the responsibilities accompanying AI advancements. 

Reader Testimonials

The book has received positive feedback for its clarity and practical approach:

"This book breaks down the complexities of AI in clear, approachable language, making it enjoyable and easy to understand—without taking up all your time." 

"An engaging, thoughtful, upbeat, well-written and overall excellent introduction to AI." 

Kindle : AI For Beginners: Grasp Generative AI and Machine Learning, Advance Your Career, and Explore the Ethical Implications of Artificial Intelligence in Just 31 Days

Hard copy : AI For Beginners: Grasp Generative AI and Machine Learning, Advance Your Career, and Explore the Ethical Implications of Artificial Intelligence in Just 31 Days

Conclusion

"Essentials of AI for Beginners" is an invaluable resource for anyone looking to understand and apply AI in their personal or professional life. Its comprehensive coverage, practical exercises, and focus on ethical considerations make it a must-read for aspiring AI enthusiasts.

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