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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:
⚡️ 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:
🔵 Mathematical and statistical calculations🔵 Processing large amounts of data🔵 Machine learning and computer vision🔵 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]
🟢 NumPy array vs Python list:🟢 NumPy - operations on the entire array at once (vectorization)🟢 List - cycles are required for element-by-element operations
绿色 NumPy - efficient storage in memory
绿色 List - stores references to objects
绿色 NumPy - built-in mathematics (linear algebra, statistics)
绿色 List - basic functionality
When to use:
🔴 Working with numerical data🔴 High performance is required🔴 Mathematics is needed: matrices, statistics, algebra🔴 Processing images or audio🔴 Integration with ML libraries (SciPy, Pandas, TensorFlow)
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The repository is completely focused on practice: it contains guides on creating parsers, bots for popular social networks, web applications, projects on Big Data and Machine Learning, games, etc. It's an excellent option not just to study the theory, but to immediately write code and build a portfolio.
I'll leave a link: GitHub📱
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This repository collects everything you need to use AI and LLM in your projects.
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→ Model training, fine-tuning, and evaluation
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120+ libraries, organized by development stages:
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→ Deploying applications with LLM and RAG
→ Fast and scalable model launch
→ Data extraction, crawlers, and scrapers
→ Creating autonomous LLM agents
→ Prompt optimization and security
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0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
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6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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Forwarded from Machine Learning with Python
These Google Colab-notebooks help to implement all machine learning algorithms from scratch 🤯
Repo: https://udlbook.github.io/udlbook/
👉 @codeprogrammer
Repo: https://udlbook.github.io/udlbook/
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Ant AI Automated Sales Robot is an intelligent robot focused on automating lead generation and sales conversion. Its core function simulates human conversation, achieving end-to-end business conversion and easily generating revenue without requiring significant time investment.
I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion"
Precise Lead Generation and Human-like Communication: Ant AI is trained on over 20 million real social chat records, enabling it to autonomously identify target customers and build trust through natural conversation, requiring no human intervention.
High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments.
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I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion"
Precise Lead Generation and Human-like Communication: Ant AI is trained on over 20 million real social chat records, enabling it to autonomously identify target customers and build trust through natural conversation, requiring no human intervention.
High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments.
24/7 Operation: Ant AI continuously searches for customers and recommends products. You only need to monitor progress via your mobile phone, requiring no additional management time.
II. Your Profit Guarantee: Low Risk, High Transparency, Zero Inventory Pressure, Stable Commission Sharing
We have established partnerships with platforms such as Shopee and Amazon, which directly provide abundant product sourcing. You don't need to worry about inventory or logistics. After each successful order, the company will charge the merchant a commission and share all profits with you. Earnings are predictable and withdrawals are convenient. Member data shows that each bot can generate $30 to $100 in profit per day. Commission income can be withdrawn to your account at any time, and the settlement process is transparent and open.
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Forwarded from Machine Learning with Python
Do you see yourself as a programmer, researcher, or engineer?
Anonymous Poll
44%
Programmer
22%
Researcher
34%
Engineer
Forwarded from Machine Learning with Python
by [@codeprogrammer]
---
🏛️ MIT OpenCourseWare – Machine Learning
---
#MachineLearning #LearnML #DataScience #AI
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Google for Developers
Machine Learning | Google for Developers
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Basic Machine Learning Algorithms
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.
👉 @DataAnalyticsX
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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