Basics of NumPy: a powerful array instead of lists
NumPy is a fundamental library for scientific computing in Python. Its core is the
Briefly about the essence:
NumPy arrays store data of one type in a continuous block of memory. This allows operations to be performed on entire arrays without loops (vectorization), which gives a huge speed boost.
Where it's really useful:
An important nuance:
Example in Python:
โก๏ธ Comparison of approaches:
When to use:
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NumPy is a fundamental library for scientific computing in Python. Its core is the
ndarray (N-dimensional array), which works much faster than ordinary lists.Briefly about the essence:
NumPy arrays store data of one type in a continuous block of memory. This allows operations to be performed on entire arrays without loops (vectorization), which gives a huge speed boost.
Where it's really useful:
๐ต Mathematical and statistical calculations๐ต Processing large amounts of data๐ต Machine learning and computer vision๐ต Scientific modeling and engineering
An important nuance:
import numpy as np
arr = np.array([1, 2, 3]) # Creating an array
arr * 2 # Vectorization: [2, 4, 6]
list = [1, 2, 3]
list * 2 # Repetition: [1, 2, 3, 1, 2, 3]
Example in Python:
# Fast operations on the entire array
import numpy as np
# Creating an array
data = np.array([1, 4, 9, 16, 25])
# Vectorized operations
sqrt_data = np.sqrt(data) # Square root of each element
mean_value = np.mean(data) # Average value
filtered = data[data > 10] # Filtering (boolean indexing)
print(sqrt_data) # [1. 2. 3. 4. 5.]
print(mean_value) # 11.0
print(filtered) # [16 25]
๐ข NumPy array vs Python list:๐ข NumPy - operations on the entire array at once (vectorization)๐ข List - cycles are required for element-by-element operations
็ปฟ่ฒ NumPy - efficient storage in memory
็ปฟ่ฒ List - stores references to objects
็ปฟ่ฒ NumPy - built-in mathematics (linear algebra, statistics)
็ปฟ่ฒ List - basic functionality
When to use:
๐ด Working with numerical data๐ด High performance is required๐ด Mathematics is needed: matrices, statistics, algebra๐ด Processing images or audio๐ด Integration with ML libraries (SciPy, Pandas, TensorFlow)
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3. Decision Tree (decision tree)
"If-then" rules, easy to explain but prone to overfitting.
4. SVM (support vector machine)
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Looks at the nearest points and votes; simple but slows down on large datasets.
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Compresses features to simplify data/visualization/remove noise.
7. Random Forest (random forest)
Many trees + averaging/voting; often a strong out-of-the-box solution.
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A fast probabilistic classifier, often good for text.
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1. Linear Regression (linear regression)
Predicts a number based on a linear relationship (example: apartment price).
2. Logistic Regression (logistic regression)
Classification, usually 0/1 (spam/not spam), outputs a probability.
3. Decision Tree (decision tree)
"If-then" rules, easy to explain but prone to overfitting.
4. SVM (support vector machine)
Seeks the boundary between classes with the maximum margin; works well on medium-sized data.
5. KNN (k-nearest neighbors)
Looks at the nearest points and votes; simple but slows down on large datasets.
6. Dimensionality Reduction (dimensionality reduction, often PCA/UMAP/t-SNE)
Compresses features to simplify data/visualization/remove noise.
7. Random Forest (random forest)
Many trees + averaging/voting; often a strong out-of-the-box solution.
8. K-means
Unsupervised clustering: divides points into k groups.
9. Naive Bayes (naive Bayes)
A fast probabilistic classifier, often good for text.
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Forwarded from Data Science Jupyter Notebooks
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