🔥 Trending Repository: best-of-ml-python

📝 Description: 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

🔗 Repository URL: https://github.com/lukasmasuch/best-of-ml-python

🌐 Website: https://ml-python.best-of.org

📖 Readme: https://github.com/lukasmasuch/best-of-ml-python#readme

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🏷️ Related Topics:

#python #nlp #data_science #machine_learning #deep_learning #tensorflow #scikit_learn #keras #ml #data_visualization #pytorch #transformer #data_analysis #gpt #automl #jax #data_visualizations #gpt_3 #chatgpt

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🧠 By: https://t.iss.one/DataScienceM

In Python, handling CSV files is straightforward using the built-in csv module for reading and writing tabular data, or pandas for advanced analysis—essential for data processing tasks like importing/exporting datasets in interviews.

# Reading CSV with csv module (basic)
import csv
with open('data.csv', 'r') as file:
    reader = csv.reader(file)
    data = list(reader)  # data = [['Name', 'Age'], ['Alice', '30'], ['Bob', '25']]

# Writing CSV with csv module
import csv
with open('output.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['Name', 'Age'])  # Header
    writer.writerows([['Alice', 30], ['Bob', 25]])  # Data rows

# Advanced: Reading with pandas (handles headers, missing values)
import pandas as pd
df = pd.read_csv('data.csv')  # df = DataFrame with columns 'Name', 'Age'
print(df.head())  # Output: First 5 rows preview

# Writing with pandas
df.to_csv('output.csv', index=False)  # Saves without row indices

#python #csv #pandas #datahandling #fileio #interviewtips

👉 @DataScience4

In Python, NumPy is the cornerstone of scientific computing, offering high-performance multidimensional arrays and tools for working with them—critical for data science interviews and real-world applications! 📊

import numpy as np

# Array Creation - The foundation of NumPy
arr = np.array([1, 2, 3])
zeros = np.zeros((2, 3))        # 2x3 matrix of zeros
ones = np.ones((2, 2), dtype=int)  # Integer matrix
arange = np.arange(0, 10, 2)    # [0 2 4 6 8]
linspace = np.linspace(0, 1, 5) # [0.  0.25 0.5  0.75 1.  ]
print(linspace)

# Array Attributes - Master your data's structure
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix.shape)  # Output: (2, 3)
print(matrix.ndim)   # Output: 2
print(matrix.dtype)  # Output: int64
print(matrix.size)   # Output: 6

# Indexing & Slicing - Precision data access
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(data[1, 2])      # Output: 6 (row 1, col 2)
print(data[0:2, 1:3])  # Output: [[2 3], [5 6]]
print(data[:, -1])     # Output: [3 6 9] (last column)

# Reshaping Arrays - Transform dimensions effortlessly
flat = np.arange(6)
reshaped = flat.reshape(2, 3)
raveled = reshaped.ravel()
print(reshaped)
# Output: [[0 1 2], [3 4 5]]
print(raveled)  # Output: [0 1 2 3 4 5]

# Stacking Arrays - Combine datasets vertically/horizontally
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(np.vstack((a, b)))  # Vertical stack
# Output: [[1 2 3], [4 5 6]]
print(np.hstack((a, b)))  # Horizontal stack
# Output: [1 2 3 4 5 6]

# Mathematical Operations - Vectorized calculations
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
print(x + y)       # Output: [5 7 9]
print(x * 2)       # Output: [2 4 6]
print(np.dot(x, y)) # Output: 32 (1*4 + 2*5 + 3*6)

# Broadcasting Magic - Operate on mismatched shapes
matrix = np.array([[1, 2, 3], [4, 5, 6]])
scalar = 10
print(matrix + scalar)
# Output: [[11 12 13], [14 15 16]]

# Aggregation Functions - Statistical power in one line
values = np.array([1, 5, 3, 9, 7])
print(np.sum(values))   # Output: 25
print(np.mean(values))  # Output: 5.0
print(np.max(values))   # Output: 9
print(np.std(values))   # Output: 2.8284271247461903

# Boolean Masking - Filter data like a pro
temperatures = np.array([18, 25, 12, 30, 22])
hot_days = temperatures > 24
print(temperatures[hot_days])  # Output: [25 30]

# Random Number Generation - Simulate real-world data
print(np.random.rand(2, 2))      # Uniform distribution
print(np.random.randn(3))        # Normal distribution
print(np.random.randint(0, 10, (2, 3)))  # Random integers

# Linear Algebra Essentials - Solve equations like a physicist
A = np.array([[3, 1], [1, 2]])
b = np.array([9, 8])
x = np.linalg.solve(A, b)
print(x)  # Output: [2. 3.] (Solution to 3x+y=9 and x+2y=8)

# Matrix inverse and determinant
print(np.linalg.inv(A))   # Output: [[ 0.4 -0.2], [-0.2  0.6]]
print(np.linalg.det(A))   # Output: 5.0

# File Operations - Save/load your computational work
data = np.array([[1, 2], [3, 4]])
np.save('array.npy', data)
loaded = np.load('array.npy')
print(np.array_equal(data, loaded))  # Output: True

# Interview Power Move: Vectorization vs Loops
# 10x faster than native Python loops!
def square_sum(n):
    arr = np.arange(n)
    return np.sum(arr ** 2)

print(square_sum(5))  # Output: 30 (0²+1²+2²+3²+4²)

# Pro Tip: Memory-efficient data processing
# Process 1GB array without loading entire dataset
large_array = np.memmap('large_data.bin', dtype='float32', mode='r', shape=(1000000, 100))
print(large_array[0:5, 0:3])  # Process small slice

By: @DataScienceQ 🚀

#Python #NumPy #DataScience #CodingInterview #MachineLearning #ScientificComputing #DataAnalysis #Programming #TechJobs #DeveloperTips

In Python, image processing unlocks powerful capabilities for computer vision, data augmentation, and automation—master these techniques to excel in ML engineering interviews and real-world applications! 🖼 

# PIL/Pillow Basics - The essential image library
from PIL import Image

# Open and display image
img = Image.open("input.jpg")
img.show()

# Convert formats
img.save("output.png")
img.convert("L").save("grayscale.jpg")  # RGB to grayscale

# Basic transformations
img.rotate(90).save("rotated.jpg")
img.resize((300, 300)).save("resized.jpg")
img.transpose(Image.FLIP_LEFT_RIGHT).save("mirrored.jpg")

more explain: https://hackmd.io/@husseinsheikho/imageprocessing

#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3

💡 Building a Simple Convolutional Neural Network (CNN)

Constructing a basic Convolutional Neural Network (CNN) is a fundamental step in deep learning for image processing. Using TensorFlow's Keras API, we can define a network with convolutional, pooling, and dense layers to classify images. This example sets up a simple CNN to recognize handwritten digits from the MNIST dataset.

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist
import numpy as np

# 1. Load and preprocess the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Reshape images for CNN: (batch_size, height, width, channels)
# MNIST images are 28x28 grayscale, so channels = 1
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

# 2. Define the CNN architecture
model = models.Sequential()

# First Convolutional Block
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))

# Second Convolutional Block
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))

# Flatten the 3D output to 1D for the Dense layers
model.add(layers.Flatten())

# Dense (fully connected) layers
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax')) # Output layer for 10 classes (digits 0-9)

# 3. Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Print a summary of the model layers
model.summary()

# 4. Train the model (uncomment to run training)
# print("\nTraining the model...")
# model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.1)

# 5. Evaluate the model (uncomment to run evaluation)
# print("\nEvaluating the model...")
# test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
# print(f"Test accuracy: {test_acc:.4f}")

Code explanation: This script defines a simple CNN using Keras. It loads and normalizes MNIST images. The Sequential model adds Conv2D layers for feature extraction, MaxPooling2D for downsampling, a Flatten layer to transition to 1D, and Dense layers for classification. The model is then compiled with an optimizer, loss function, and metrics, and a summary of its architecture is printed. Training and evaluation steps are included as commented-out examples.

#Python #DeepLearning #CNN #Keras #TensorFlow

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By: @CodeProgrammer ✨

🤖🧠 Reflex: Build Full-Stack Web Apps in Pure Python — Fast, Flexible and Powerful

🗓️ 29 Oct 2025
📚 AI News & Trends

Building modern web applications has traditionally required mastering multiple languages and frameworks from JavaScript for the frontend to Python, Java or Node.js for the backend. For many developers, switching between different technologies can slow down productivity and increase complexity. Reflex eliminates that problem. It is an innovative open-source full-stack web framework that allows developers to ...

#Reflex #FullStack #WebDevelopment #Python #OpenSource #WebApps

💡 NumPy Tip: Efficient Filtering with Boolean Masks

Avoid slow Python loops for filtering data. Instead, create a "mask" array of True/False values based on a condition. Applying this mask to your original array instantly selects only the elements where the mask is True, which is significantly faster.

import numpy as np

# Create an array of data
data = np.array([10, 55, 8, 92, 43, 77, 15])

# Create a boolean mask for values greater than 50
high_values_mask = data > 50

# Use the mask to select elements
filtered_data = data[high_values_mask]

print(filtered_data)
# Output: [55 92 77]

Code explanation: A NumPy array data is created. Then, a boolean array high_values_mask is generated, which is True for every element in data greater than 50. This mask is used as an index to efficiently extract and print only those matching elements from the original array.

#Python #NumPy #DataScience #CodingTips #Programming

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By: @CodeProgrammer ✨

💡 Python F-Strings Cheatsheet

F-strings (formatted string literals) provide a concise and powerful way to embed expressions inside string literals for formatting. Just prefix the string with an f or F.

1. Basic Variable and Expression Embedding

name = "Alice"
quantity = 5
print(f"Hello, {name}. You have {quantity * 2} items in your cart.")
# Output: Hello, Alice. You have 10 items in your cart.

• Place variables or expressions directly inside curly braces {}. Python evaluates the expression and inserts the result into the string.

2. Number Formatting
Control the appearance of numbers, such as padding with zeros or setting decimal precision.

pi_value = 3.14159
order_id = 42
print(f"Pi: {pi_value:.2f}")
print(f"Order ID: {order_id:04d}")
# Output:
# Pi: 3.14
# Order ID: 0042

• :.2f formats the float to have exactly two decimal places.
• :04d formats the integer to be at least 4 digits long, padding with leading zeros if necessary.

3. Alignment and Padding
Align text within a specified width, which is useful for creating tables or neatly formatted output.

item = "Docs"
print(f"|{item:<10}|") # Left-aligned
print(f"|{item:^10}|") # Center-aligned
print(f"|{item:>10}|") # Right-aligned
# Output:
# |Docs      |
# |   Docs   |
# |      Docs|

• Use < for left, ^ for center, and > for right alignment, followed by the total width.

4. Date and Time Formatting
Directly format datetime objects within an f-string.

from datetime import datetime
now = datetime.now()
print(f"Current time: {now:%Y-%m-%d %H:%M}")
# Output: Current time: 2023-10-27 14:30

• Use a colon : followed by standard strftime formatting codes to display dates and times as you wish.

#Python #Programming #CodingTips #FStrings #PythonTips

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By: @CodeProgrammer ✨

💡 Keras: Building Neural Networks Simply

Keras is a high-level deep learning API, now part of TensorFlow, designed for fast and easy experimentation. This guide covers the fundamental workflow: defining, compiling, training, and using a neural network model.

from tensorflow import keras
from tensorflow.keras import layers

# Define a Sequential model
model = keras.Sequential([
    # Input layer with 64 neurons, expecting flat input data
    layers.Dense(64, activation="relu", input_shape=(784,)),
    # A hidden layer with 32 neurons
    layers.Dense(32, activation="relu"),
    # Output layer with 10 neurons for 10-class classification
    layers.Dense(10, activation="softmax")
])

model.summary()

• Model Definition: keras.Sequential creates a simple, layer-by-layer model.
• layers.Dense is a standard fully-connected layer. The first layer must specify the input_shape.
• activation functions like "relu" introduce non-linearity, while "softmax" is used on the output layer for multi-class classification to produce probabilities.

# (Continuing from the previous step)
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

print("Model compiled successfully.")

• Compilation: .compile() configures the model for training.
• optimizer is the algorithm used to update the model's weights (e.g., 'adam' is a popular choice).
• loss is the function the model tries to minimize during training. sparse_categorical_crossentropy is common for integer-based classification labels.
• metrics are used to monitor the training and testing steps. Here, we track accuracy.

import numpy as np

# Create dummy training data
x_train = np.random.random((1000, 784))
y_train = np.random.randint(10, size=(1000,))

# Train the model
history = model.fit(
    x_train,
    y_train,
    epochs=5,
    batch_size=32,
    verbose=0 # Hides the progress bar for a cleaner output
)

print(f"Training complete. Final accuracy: {history.history['accuracy'][-1]:.4f}")
# Output (will vary):
# Training complete. Final accuracy: 0.4570

• Training: The .fit() method trains the model on your data.
• x_train and y_train are your input features and target labels.
• epochs defines how many times the model will see the entire dataset.
• batch_size is the number of samples processed before the model is updated.

# Create a single dummy sample to test
x_test = np.random.random((1, 784))

# Get the model's prediction
predictions = model.predict(x_test)
predicted_class = np.argmax(predictions[0])

print(f"Predicted class: {predicted_class}")
print(f"Confidence scores: {predictions[0].round(2)}")
# Output (will vary):
# Predicted class: 3
# Confidence scores: [0.09 0.1  0.1  0.12 0.1  0.09 0.11 0.1  0.09 0.1 ]

• Prediction: .predict() is used to make predictions on new, unseen data.
• For a classification model with a softmax output, this returns an array of probabilities for each class.
• np.argmax() is used to find the index (the class) with the highest probability score.

#Keras #TensorFlow #DeepLearning #MachineLearning #Python

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By: @CodeProgrammer ✨