Machine Learning with Python

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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Matplotlib Cheatsheet (⭐️⭐️⭐️⭐️⭐️)

💡 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 ━━━━━━━━━━━━━━━ By: @CodeProgrammer ✨

💡 Python: Converting Numbers to Human-Readable Words Transforming numerical values into their word equivalents is crucial for various applications like financial reports, check writing, educational software, or enhancing accessibility. While complex to implement from scratch for all cases, Python's num2words library provides a robust and easy solution. Install it with pip install num2words.

from num2words import num2words

# Example 1: Basic integer
number1 = 123
words1 = num2words(number1)
print(f"'{number1}' in words: {words1}")

# Example 2: Larger integer
number2 = 543210
words2 = num2words(number2, lang='en') # Explicitly set language
print(f"'{number2}' in words: {words2}")

# Example 3: Decimal number
number3 = 100.75
words3 = num2words(number3)
print(f"'{number3}' in words: {words3}")

# Example 4: Negative number
number4 = -45
words4 = num2words(number4)
print(f"'{number4}' in words: {words4}")

# Example 5: Number for an ordinal form
number5 = 3
words5 = num2words(number5, to='ordinal')
print(f"Ordinal '{number5}' in words: {words5}")

Code explanation: This script uses the num2words library to convert various integers, decimals, and negative numbers into their English word representations. It also demonstrates how to generate ordinal forms (third instead of three) and explicitly set the output language. #Python #TextProcessing #NumberToWords #num2words #DataManipulation ━━━━━━━━━━━━━━━ By: @CodeProgrammer ✨

💡 Python: Converting Numbers to Human-Readable Words Transforming numerical values into their word equivalents is crucial for various applications like financial reports, check writing, educational software, or enhancing accessibility. While complex to implement from scratch for all cases, Python's num2words library provides a robust and easy solution. Install it with pip install num2words.

from num2words import num2words

# Example 1: Basic integer
number1 = 123
words1 = num2words(number1)
print(f"'{number1}' in words: {words1}")

# Example 2: Larger integer
number2 = 543210
words2 = num2words(number2, lang='en') # Explicitly set language
print(f"'{number2}' in words: {words2}")

# Example 3: Decimal number
number3 = 100.75
words3 = num2words(number3)
print(f"'{number3}' in words: {words3}")

# Example 4: Negative number
number4 = -45
words4 = num2words(number4)
print(f"'{number4}' in words: {words4}")

# Example 5: Number for an ordinal form
number5 = 3
words5 = num2words(number5, to='ordinal')
print(f"Ordinal '{number5}' in words: {words5}")

Code explanation: This script uses the num2words library to convert various integers, decimals, and negative numbers into their English word representations. It also demonstrates how to generate ordinal forms (third instead of three) and explicitly set the output language. #Python #TextProcessing #NumberToWords #num2words #DataManipulation ━━━━━━━━━━━━━━━ By: @CodeProgrammer ✨

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🤖🧠 Microsoft Data Formulator: Revolutionizing AI-Powered Data Visualization 🗓️ 28 Oct 2025 📚 AI News & Trends In today’s data-driven world, visualization is everything. Whether you’re a business analyst, data scientist or researcher, the ability to convert raw data into meaningful visuals can define the success of your decisions. That’s where Microsoft’s Data Formulator steps in a cutting-edge, open-source platform designed to empower analysts to create rich, AI-assisted visualizations effortlessly. Developed by ... #Microsoft #DataVisualization #AI #DataScience #OpenSource #Analytics

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