🔖 40 NumPy methods that cover 95% of tasks

A convenient cheat sheet for those who work with data analysis and ML.

Here are collected the main functions for:

▶️ Creating and modifying arrays;
▶️ Mathematical operations;
▶️ Working with matrices and vectors;
▶️ Sorting and searching for values.

Save it for yourself — it will come in handy when working with NumPy.

tags: #NumPy #Python

➡ @DataScienceM

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ML engineers, this is for you: an interactive math tutorial for machine learning

Recently, they posted several more blogs on the basics of mathematical analysis for machine learning, with interactive simulations.

Among the topics:

- backprop and gradient descent
- local minima and saddle points
- vector fields
- Taylor series
- Jacobian and Hessian
- partial derivatives

The material is specifically focused on the ML context, with an emphasis on clarity and practical understanding. ✌️

Let's practice here

👉 @codeprogrammer

For beginners: a free online course on Python programming

On the site, you can run code directly in the browser, solve problems, and learn the basics of the language step by step

Start your improvement 👍

👉 @codeprogrammer

📐 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐒𝐕𝐌)⁣

🔹 What I covered today⁣
What SVM is and how it works⁣
Concept of hyperplane, margin, and support vectors⁣
Hard margin vs Soft margin⁣
Role of kernel trick⁣
⁣
When SVM performs better than other classifiers⁣
⁣
🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)⁣
⁣
1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘝𝘦𝘤𝘵𝘰𝘳 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 (𝘚𝘝𝘔)?⁣
2️⃣ 𝘞𝘩𝘢𝘵 𝘢𝘳𝘦 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘷𝘦𝘤𝘵𝘰𝘳𝘴?⁣
3️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘮𝘢𝘳𝘨𝘪𝘯 𝘪𝘯 𝘚𝘝𝘔?⁣
4️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘩𝘢𝘳𝘥 𝘮𝘢𝘳𝘨𝘪𝘯 𝘢𝘯𝘥 𝘴𝘰𝘧𝘵 𝘮𝘢𝘳𝘨𝘪𝘯?⁣
5️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘬𝘦𝘳𝘯𝘦𝘭 𝘵𝘳𝘪𝘤𝘬 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘪𝘴 𝘪𝘵 𝘯𝘦𝘦𝘥𝘦𝘥?⁣
6️⃣ 𝘊𝘰𝘮𝘮𝘰𝘯 𝘬𝘦𝘳𝘯𝘦𝘭𝘴 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘚𝘝𝘔 (𝘓𝘪𝘯𝘦𝘢𝘳, 𝘗𝘰𝘭𝘺𝘯𝘰𝘮𝘪𝘢𝘭, 𝘙𝘉𝘍)?⁣
7️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘳𝘰𝘭𝘦 𝘰𝘧 𝘊 (𝘳𝘦𝘨𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘱𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳)?⁣
8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘨𝘢𝘮𝘮𝘢 𝘪𝘯 𝘙𝘉𝘍 𝘬𝘦𝘳𝘯𝘦𝘭?⁣
9️⃣ 𝘊𝘢𝘯 #𝘚𝘝𝘔 𝘣𝘦 𝘶𝘴𝘦𝘥 𝘧𝘰𝘳 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯? (𝘚𝘝𝘙)⁣
🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 𝘚𝘝𝘔?⁣

https://t.me/CodeProgrammer ✈️

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

📈 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧⁣⁣

Why Logistic Regression is not regression⁣⁣
How Sigmoid (Logistic) function works⁣⁣
Binary vs Multiclass Logistic Regression⁣⁣
Decision boundaries and probability interpretation⁣⁣
Where Logistic Regression beats complex models⁣⁣
⁣⁣
🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)⁣⁣
⁣⁣
1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?⁣⁣
2️⃣ 𝘞𝘩𝘺 𝘪𝘴 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯 𝘶𝘴𝘦𝘥 𝘧𝘰𝘳 𝘤𝘭𝘢𝘴𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯, 𝘯𝘰𝘵 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?⁣⁣
3️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘚𝘪𝘨𝘮𝘰𝘪𝘥 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘪𝘴 𝘪𝘵 𝘯𝘦𝘦𝘥𝘦𝘥?⁣⁣
4️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘓𝘰𝘨 𝘓𝘰𝘴𝘴 / 𝘊𝘳𝘰𝘴𝘴-𝘌𝘯𝘵𝘳𝘰𝘱𝘺 𝘓𝘰𝘴𝘴?⁣⁣
5️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯 𝘢𝘯𝘥 𝘓𝘪𝘯𝘦𝘢𝘳 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?⁣⁣
6️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘺?⁣⁣
7️⃣ 𝘏𝘰𝘸 𝘥𝘰𝘦𝘴 𝘙𝘦𝘨𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯 (𝘓1 𝘷𝘴 𝘓2) 𝘸𝘰𝘳𝘬 𝘪𝘯 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?⁣⁣
8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘖𝘥𝘥𝘴 𝘙𝘢𝘵𝘪𝘰 𝘢𝘯𝘥 𝘩𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘪𝘯𝘵𝘦𝘳𝘱𝘳𝘦𝘵 𝘤𝘰𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵𝘴?⁣⁣
9️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘩𝘢𝘯𝘥𝘭𝘦 𝘤𝘭𝘢𝘴𝘴 𝘪𝘮𝘣𝘢𝘭𝘢𝘯𝘤𝘦?⁣⁣
🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?⁣⁣

https://t.me/CodeProgrammer ✅