Machine Learning with Python

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🗓️ 11 Nov 2025
📚 AI News & Trends

The field of artificial intelligence has witnessed a remarkable evolution and at the heart of this transformation lies the Transformer architecture. Introduced by Vaswani et al. in 2017, the paper “Attention Is All You Need” redefined the foundations of natural language processing (NLP) and sequence modeling. Unlike its predecessors – recurrent and convolutional neural networks, ...

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By: @CodeProgrammer ✨

Python Interview Codes Cheatsheet

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🏆 150 Python Clean Code Essentials

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By: @CodeProgrammer ✨

Python is a high-level, interpreted programming language known for its simplicity, readability, and
 versatility. It was first released in 1991 by Guido van Rossum and has since become one of the most
 popular programming languages in the world.
 Python’s syntax emphasizes readability, with code written in a clear and concise manner using whitespace and indentation to define blocks of code. It is an interpreted language, meaning that
 code is executed line-by-line rather than compiled into machine code. This makes it easy to write and test code quickly, without needing to worry about the details of low-level hardware.
 Python is a general-purpose language, meaning that it can be used for a wide variety of applications, from web development to scientific computing to artificial intelligence and machine learning. Its simplicity and ease of use make it a popular choice for beginners, while its power and flexibility make it a favorite of experienced developers.
 Python’s standard library contains a wide range of modules and packages, providing support for
 everything from basic data types and control structures to advanced data manipulation and visualization. Additionally, there are countless third-party packages available through Python’s package manager, pip, allowing developers to easily extend Python’s capabilities to suit their needs.
 Overall, Python’s combination of simplicity, power, and flexibility makes it an ideal language for a wide range of applications and skill levels.

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• Apply a simple blur filter.

from PIL import ImageFilter
blurred_img = img.filter(ImageFilter.BLUR)

• Apply a box blur with a given radius.

box_blur = img.filter(ImageFilter.BoxBlur(5))

• Apply a Gaussian blur.

gaussian_blur = img.filter(ImageFilter.GaussianBlur(radius=2))

• Sharpen the image.

sharpened = img.filter(ImageFilter.SHARPEN)

• Find edges.

edges = img.filter(ImageFilter.FIND_EDGES)

• Enhance edges.

edge_enhanced = img.filter(ImageFilter.EDGE_ENHANCE)

• Emboss the image.

embossed = img.filter(ImageFilter.EMBOSS)

• Find contours.

contours = img.filter(ImageFilter.CONTOUR)

VII. Image Enhancement (ImageEnhance)

• Adjust color saturation.

from PIL import ImageEnhance
enhancer = ImageEnhance.Color(img)
vibrant_img = enhancer.enhance(2.0)

• Adjust brightness.

enhancer = ImageEnhance.Brightness(img)
bright_img = enhancer.enhance(1.5)

• Adjust contrast.

enhancer = ImageEnhance.Contrast(img)
contrast_img = enhancer.enhance(1.5)

• Adjust sharpness.

enhancer = ImageEnhance.Sharpness(img)
sharp_img = enhancer.enhance(2.0)

VIII. Drawing (ImageDraw & ImageFont)

• Draw text on an image.

from PIL import ImageDraw, ImageFont
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("arial.ttf", 36)
draw.text((10, 10), "Hello", font=font, fill="red")

• Draw a line.

draw.line((0, 0, 100, 200), fill="blue", width=3)

• Draw a rectangle (outline).

draw.rectangle([10, 10, 90, 60], outline="green", width=2)

• Draw a filled ellipse.

draw.ellipse([100, 100, 180, 150], fill="yellow")

• Draw a polygon.

draw.polygon([(10,10), (20,50), (60,10)], fill="purple")

#Python #Pillow #ImageProcessing #PIL #CheatSheet

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By: @CodeProgrammer ✨

💡 Top 50 Pillow Operations for Image Processing

I. File & Basic Operations

• Open an image file.

from PIL import Image
img = Image.open("image.jpg")

• Save an image.

img.save("new_image.png")

• Display an image (opens in default viewer).

img.show()

• Create a new blank image.

new_img = Image.new("RGB", (200, 100), "blue")

• Get image format (e.g., 'JPEG').

print(img.format)

• Get image dimensions as a (width, height) tuple.

width, height = img.size

• Get pixel format (e.g., 'RGB', 'L' for grayscale).

print(img.mode)

• Convert image mode.

grayscale_img = img.convert("L")

• Get a pixel's color value at (x, y).

r, g, b = img.getpixel((10, 20))

• Set a pixel's color value at (x, y).

img.putpixel((10, 20), (255, 0, 0))

II. Cropping, Resizing & Pasting

• Crop a rectangular region.

box = (100, 100, 400, 400)
cropped_img = img.crop(box)

• Resize an image to an exact size.

resized_img = img.resize((200, 200))

• Create a thumbnail (maintains aspect ratio).

img.thumbnail((128, 128))

• Paste one image onto another.

img.paste(another_img, (50, 50))

III. Rotation & Transformation

• Rotate an image (counter-clockwise).

rotated_img = img.rotate(45, expand=True)

• Flip an image horizontally.

flipped_img = img.transpose(Image.FLIP_LEFT_RIGHT)

• Flip an image vertically.

flipped_img = img.transpose(Image.FLIP_TOP_BOTTOM)

• Rotate by 90, 180, or 270 degrees.

img_90 = img.transpose(Image.ROTATE_90)

• Apply an affine transformation.

transformed = img.transform(img.size, Image.AFFINE, (1, 0.5, 0, 0, 1, 0))

IV. ImageOps Module Helpers

• Invert image colors.

from PIL import ImageOps
inverted_img = ImageOps.invert(img)

• Flip an image horizontally (mirror).

mirrored_img = ImageOps.mirror(img)

• Flip an image vertically.

flipped_v_img = ImageOps.flip(img)

• Convert to grayscale.

grayscale = ImageOps.grayscale(img)

• Colorize a grayscale image.

colorized = ImageOps.colorize(grayscale, black="blue", white="yellow")

• Reduce the number of bits for each color channel.

posterized = ImageOps.posterize(img, 4)

• Auto-adjust image contrast.

adjusted_img = ImageOps.autocontrast(img)

• Equalize the image histogram.

equalized_img = ImageOps.equalize(img)

• Add a border to an image.

bordered = ImageOps.expand(img, border=10, fill='black')

V. Color & Pixel Operations

• Split image into individual bands (e.g., R, G, B).

r, g, b = img.split()

• Merge bands back into an image.

merged_img = Image.merge("RGB", (r, g, b))

• Apply a function to each pixel.

brighter_img = img.point(lambda i: i * 1.2)

• Get a list of colors used in the image.

colors = img.getcolors(maxcolors=256)

• Blend two images with alpha compositing.

# Both images must be in RGBA mode
blended = Image.alpha_composite(img1_rgba, img2_rgba)

VI. Filters (ImageFilter)

💡 Top 50 Pillow Operations for Image Processing
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Coefficient: 1.0

#97. LogisticRegression()
Implements Logistic Regression for classification.

from sklearn.linear_model import LogisticRegression
X = [[-1], [0], [1], [2]]
y = [0, 0, 1, 1]
clf = LogisticRegression().fit(X, y)
print(f"Prediction for [[-2]]: {clf.predict([[-2]])}")

Prediction for [[-2]]: [0]

#98. KMeans()
K-Means clustering algorithm.

from sklearn.cluster import KMeans
X = [[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]
kmeans = KMeans(n_clusters=2, n_init='auto').fit(X)
print(kmeans.labels_)

[0 0 0 1 1 1]
(Note: Cluster labels may be flipped, e.g., [1 1 1 0 0 0])

#99. accuracy_score()
Calculates the accuracy classification score.

from sklearn.metrics import accuracy_score
y_true = [0, 1, 1, 0]
y_pred = [0, 1, 0, 0]
print(accuracy_score(y_true, y_pred))

0.75

#100. confusion_matrix()
Computes a confusion matrix to evaluate the accuracy of a classification.

from sklearn.metrics import confusion_matrix
y_true = [0, 1, 0, 1]
y_pred = [1, 1, 0, 1]
print(confusion_matrix(y_true, y_pred))

[[1 1]
 [0 2]]

━━━━━━━━━━━━━━━
By: @CodeProgrammer ✨

import seaborn as sns
import pandas as pd
df = pd.DataFrame(np.random.randn(100, 4), columns=['A', 'B', 'C', 'D'])
# sns.pairplot(df) # This line would generate the plot
print("Output: A figure grid opens showing scatterplots for each pair of variables.")

Output: A figure grid opens showing scatterplots for each pair of variables.

#88. sns.countplot()
Shows the counts of observations in each categorical bin using bars.

import seaborn as sns
import pandas as pd
df = pd.DataFrame({'category': ['A', 'B', 'A', 'C', 'A', 'B']})
sns.countplot(x='category', data=df)
print("Output: A figure window opens showing a count plot.")

Output: A figure window opens showing a count plot.

#89. sns.jointplot()
Draws a plot of two variables with bivariate and univariate graphs.

import seaborn as sns
import pandas as pd
df = pd.DataFrame({'x': range(50), 'y': range(50) + np.random.randn(50)})
# sns.jointplot(x='x', y='y', data=df) # This line would generate the plot
print("Output: A figure shows a scatter plot with histograms for each axis.")

Output: A figure shows a scatter plot with histograms for each axis.

#90. plt.show()
Displays all open figures.

import matplotlib.pyplot as plt
plt.plot([1, 2, 3])
# plt.show() # In a script, this is essential to see the plot.
print("Executes the command to render and display the plot.")

Executes the command to render and display the plot.

---
#DataAnalysis #ScikitLearn #Modeling #Preprocessing

Part 9: Scikit-learn - Modeling & Preprocessing

#91. train_test_split()
Splits arrays or matrices into random train and test subsets.

from sklearn.model_selection import train_test_split
import numpy as np
X, y = np.arange(10).reshape((5, 2)), range(5)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
print(f"X_train shape: {X_train.shape}")
print(f"X_test shape: {X_test.shape}")

X_train shape: (3, 2)
X_test shape: (2, 2)

#92. StandardScaler()
Standardizes features by removing the mean and scaling to unit variance.

from sklearn.preprocessing import StandardScaler
data = [[0, 0], [0, 0], [1, 1], [1, 1]]
scaler = StandardScaler()
print(scaler.fit_transform(data))

[[-1. -1.]
 [-1. -1.]
 [ 1.  1.]
 [ 1.  1.]]

#93. MinMaxScaler()
Transforms features by scaling each feature to a given range, typically [0, 1].

from sklearn.preprocessing import MinMaxScaler
data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
scaler = MinMaxScaler()
print(scaler.fit_transform(data))

[[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]

#94. LabelEncoder()
Encodes target labels with values between 0 and n_classes-1.

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
encoded = le.fit_transform(['paris', 'tokyo', 'paris'])
print(encoded)

[0 1 0]

#95. OneHotEncoder()
Encodes categorical features as a one-hot numeric array.

from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder()
X = [['Male'], ['Female'], ['Female']]
print(enc.fit_transform(X).toarray())

[[0. 1.]
 [1. 0.]
 [1. 0.]]

#96. LinearRegression()
Ordinary least squares Linear Regression model.

from sklearn.linear_model import LinearRegression
X = [[0], [1], [2]]
y = [0, 1, 2]
reg = LinearRegression().fit(X, y)
print(f"Coefficient: {reg.coef_[0]}")

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.std(arr))

1.4142135623730951

#74. np.sum()
Sums array elements over a given axis.

import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(np.sum(arr))

10

#75. np.min()
Returns the minimum of an array or minimum along an axis.

import numpy as np
arr = np.array([5, 2, 8, 1])
print(np.min(arr))

1

#76. np.max()
Returns the maximum of an array or maximum along an axis.

import numpy as np
arr = np.array([5, 2, 8, 1])
print(np.max(arr))

8

#77. np.sqrt()
Returns the non-negative square-root of an array, element-wise.

import numpy as np
arr = np.array([4, 9, 16])
print(np.sqrt(arr))

[2. 3. 4.]

#78. np.log()
Calculates the natural logarithm, element-wise.

import numpy as np
arr = np.array([1, np.e, np.e**2])
print(np.log(arr))

[0. 1. 2.]

#79. np.dot()
Calculates the dot product of two arrays.

import numpy as np
a = np.array([1, 2])
b = np.array([3, 4])
print(np.dot(a, b))

11

#80. np.where()
Returns elements chosen from x or y depending on a condition.

import numpy as np
arr = np.array([10, 5, 20, 15])
print(np.where(arr > 12, 'High', 'Low'))

['Low' 'Low' 'High' 'High']

---
#DataAnalysis #Matplotlib #Seaborn #Visualization

Part 8: Matplotlib & Seaborn - Data Visualization

#81. plt.plot()
Plots y versus x as lines and/or markers.

import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
# In a real script, you would call plt.show()
print("Output: A figure window opens displaying a line plot.")

Output: A figure window opens displaying a line plot.

#82. plt.scatter()
A scatter plot of y vs. x with varying marker size and/or color.

import matplotlib.pyplot as plt
plt.scatter([1, 2, 3, 4], [1, 4, 9, 16])
print("Output: A figure window opens displaying a scatter plot.")

Output: A figure window opens displaying a scatter plot.

#83. plt.hist()
Computes and draws the histogram of x.

import matplotlib.pyplot as plt
import numpy as np
data = np.random.randn(1000)
plt.hist(data, bins=30)
print("Output: A figure window opens displaying a histogram.")

Output: A figure window opens displaying a histogram.

#84. plt.bar()
Makes a bar plot.

import matplotlib.pyplot as plt
plt.bar(['A', 'B', 'C'], [10, 15, 7])
print("Output: A figure window opens displaying a bar chart.")

Output: A figure window opens displaying a bar chart.

#85. plt.boxplot()
Makes a box and whisker plot.

import matplotlib.pyplot as plt
import numpy as np
data = [np.random.normal(0, std, 100) for std in range(1, 4)]
plt.boxplot(data)
print("Output: A figure window opens displaying a box plot.")

Output: A figure window opens displaying a box plot.

#86. sns.heatmap()
Plots rectangular data as a color-encoded matrix.

import seaborn as sns
import numpy as np
data = np.random.rand(10, 12)
sns.heatmap(data)
print("Output: A figure window opens displaying a heatmap.")

Output: A figure window opens displaying a heatmap.

#87. sns.pairplot()
Plots pairwise relationships in a dataset.

import pandas as pd
df1 = pd.DataFrame({'val1': [1, 2]}, index=['A', 'B'])
df2 = pd.DataFrame({'val2': [3, 4]}, index=['A', 'B'])
joined = df1.join(df2)
print(joined)

val1  val2
A     1     3
B     2     4

#59. pd.get_dummies()
Converts categorical variable into dummy/indicator variables (one-hot encoding).

import pandas as pd
s = pd.Series(list('abca'))
dummies = pd.get_dummies(s)
print(dummies)

a  b  c
0  1  0  0
1  0  1  0
2  0  0  1
3  1  0  0

#60. df.nlargest()
Returns the first n rows ordered by columns in descending order.

import pandas as pd
df = pd.DataFrame({'population': [100, 500, 200, 800]})
print(df.nlargest(2, 'population'))

population
3         800
1         500

---
#DataAnalysis #NumPy #Arrays

Part 6: NumPy - Array Creation & Manipulation

#61. np.array()
Creates a NumPy ndarray.

import numpy as np
arr = np.array([1, 2, 3])
print(arr)

[1 2 3]

#62. np.arange()
Returns an array with evenly spaced values within a given interval.

import numpy as np
arr = np.arange(0, 5)
print(arr)

[0 1 2 3 4]

#63. np.linspace()
Returns an array with evenly spaced numbers over a specified interval.

import numpy as np
arr = np.linspace(0, 10, 5)
print(arr)

[ 0.   2.5  5.   7.5 10. ]

#64. np.zeros()
Returns a new array of a given shape and type, filled with zeros.

import numpy as np
arr = np.zeros((2, 3))
print(arr)

[[0. 0. 0.]
 [0. 0. 0.]]

#65. np.ones()
Returns a new array of a given shape and type, filled with ones.

import numpy as np
arr = np.ones((2, 3))
print(arr)

[[1. 1. 1.]
 [1. 1. 1.]]

#66. np.random.rand()
Creates an array of the given shape and populates it with random samples from a uniform distribution over [0, 1).

import numpy as np
arr = np.random.rand(2, 2)
print(arr)

[[0.13949386 0.2921446 ]
 [0.52273283 0.77122228]]
(Note: Output values will be random)

#67. arr.reshape()
Gives a new shape to an array without changing its data.

import numpy as np
arr = np.arange(6)
reshaped_arr = arr.reshape((2, 3))
print(reshaped_arr)

[[0 1 2]
 [3 4 5]]

#68. np.concatenate()
Joins a sequence of arrays along an existing axis.

import numpy as np
a = np.array([[1, 2]])
b = np.array([[3, 4]])
print(np.concatenate((a, b), axis=0))

[[1 2]
 [3 4]]

#69. np.vstack()
Stacks arrays in sequence vertically (row wise).

import numpy as np
a = np.array([1, 2])
b = np.array([3, 4])
print(np.vstack((a, b)))

[[1 2]
 [3 4]]

#70. np.hstack()
Stacks arrays in sequence horizontally (column wise).

import numpy as np
a = np.array([1, 2])
b = np.array([3, 4])
print(np.hstack((a, b)))

[1 2 3 4]

---
#DataAnalysis #NumPy #Math #Statistics

Part 7: NumPy - Mathematical & Statistical Functions

#71. np.mean()
Computes the arithmetic mean along the specified axis.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.mean(arr))

3.0

#72. np.median()
Computes the median along the specified axis.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.median(arr))

3.0

#73. np.std()
Computes the standard deviation along the specified axis.

import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B', 'A']})
print(df.groupby('Team').size())

Team
A    3
B    2
dtype: int64

#49. groupby.count()
Computes the count of non-NA cells for each group.

import pandas as pd
import numpy as np
df = pd.DataFrame({'Team': ['A', 'B', 'A'], 'Score': [1, np.nan, 3]})
print(df.groupby('Team').count())

Score
Team       
A         2
B         0

#50. groupby.mean()
Computes the mean of group values.

import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
print(df.groupby('Team').mean())

Points
Team        
A         11
B          7

#51. groupby.sum()
Computes the sum of group values.

import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
print(df.groupby('Team').sum())

Points
Team        
A         22
B         14

#52. groupby.min()
Computes the minimum of group values.

import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
print(df.groupby('Team').min())

Points
Team        
A         10
B          6

#53. groupby.max()
Computes the maximum of group values.

import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
print(df.groupby('Team').max())

Points
Team        
A         12
B          8

#54. df.pivot_table()
Creates a spreadsheet-style pivot table as a DataFrame.

import pandas as pd
df = pd.DataFrame({'A': ['foo', 'foo', 'bar'], 'B': ['one', 'two', 'one'], 'C': [1, 2, 3]})
pivot = df.pivot_table(values='C', index='A', columns='B')
print(pivot)

B    one  two
A            
bar  3.0  NaN
foo  1.0  2.0

#55. pd.crosstab()
Computes a cross-tabulation of two (or more) factors.

import pandas as pd
df = pd.DataFrame({'A': ['foo', 'foo', 'bar'], 'B': ['one', 'two', 'one']})
crosstab = pd.crosstab(df.A, df.B)
print(crosstab)

B    one  two
A            
bar    1    0
foo    1    1

---
#DataAnalysis #Pandas #Merging #Joining

Part 5: Pandas - Merging & Concatenating

#56. pd.merge()
Merges DataFrame or named Series objects with a database-style join.

import pandas as pd
df1 = pd.DataFrame({'key': ['A', 'B'], 'val1': [1, 2]})
df2 = pd.DataFrame({'key': ['A', 'B'], 'val2': [3, 4]})
merged = pd.merge(df1, df2, on='key')
print(merged)

key  val1  val2
0   A     1     3
1   B     2     4

#57. pd.concat()
Concatenates pandas objects along a particular axis.

import pandas as pd
df1 = pd.DataFrame({'A': [1, 2]})
df2 = pd.DataFrame({'A': [3, 4]})
concatenated = pd.concat([df1, df2])
print(concatenated)

A
0  1
1  2
0  3
1  4

#58. df.join()
Joins columns with other DataFrame(s) on index or on a key column.