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📐 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⁣
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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?⁣

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1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
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✅ 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 ✅