11. Feature Engineering and Data Preparation

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12. Cross Validation , Grid Search, and the Linear Regression Project

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13. Logistic Regression - Part 01

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13. Logistic Regression - Part 02

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14. KNN - K Nearest Neighbors

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15. Support Vector Machines

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16. Tree Based Methods Decision Tree Learning

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17. Random Forests

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🔰 Machine Learning A-Z™: Hands-On Python & R In Data Science

⏱ 44 Hours 📦 251 Lessons

📃 Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

📃 Taught By: Kirill Eremenko, Hadelin de Ponteves

🌐 Download Full Course: https://news.1rj.ru/str/+xIYRmZxpAog5YTdk

⭐️ Python Courses:
https://news.1rj.ru/str/Python53

1. Welcome to the course!

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2. Part 1 Data Preprocessing

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3. Part 2 Regression

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4. Simple Linear Regression

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5. Multiple Linear Regression

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6. Polynomial Regression

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7. Support Vector Regression (SVR)

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8. Decision Tree Regression

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9. Random Forest Regression

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10. Evaluating Regression Models Performance

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11. Part 3 Classification

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12. Logistic Regression

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