Forwarded from Python Projects & Resources
๐ฒ ๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐ง๐ผ ๐๐ต๐ฎ๐ป๐ด๐ฒ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐๐ป ๐ฎ๐ฌ๐ฎ๐ฑ ๐
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Forwarded from Artificial Intelligence
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๐๐๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ๐
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๐ Dive into the world of Data Analytics with these 6 free courses by IBM!
Gain practical knowledge and stand out in your career with tools designed for real-world applications.
All courses come with expert guidance and are free to access!๐
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Time Complexity of 10 Most Popular ML Algorithms
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When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.
For instance,
1๏ธโฃ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.
2๏ธโฃ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.
3๏ธโฃ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.
4๏ธโฃ K-Nearest Neighbours is simple but can become slow with large datasets due to distance calculations.
5๏ธโฃ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.
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When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.
For instance,
1๏ธโฃ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.
2๏ธโฃ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.
3๏ธโฃ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.
4๏ธโฃ K-Nearest Neighbours is simple but can become slow with large datasets due to distance calculations.
5๏ธโฃ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.
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๐ญ๐ฌ๐ฌ๐ฌ+ ๐๐ฟ๐ฒ๐ฒ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฏ๐ ๐๐ป๐ณ๐ผ๐๐๐ โ ๐๐ฒ๐ฎ๐ฟ๐ป, ๐๐ฟ๐ผ๐, ๐ฆ๐๐ฐ๐ฐ๐ฒ๐ฒ๐ฑ!๐
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๐ Looking to upgrade your skills without spending a rupee?๐ฐ
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Python Full Stack Developer Roadmap:
Stage 1: HTML โ Learn webpage basics.
Stage 2: CSS โ Style web pages.
Stage 3: JavaScript โ Add interactivity.
Stage 4: Git + GitHub โ Manage code versions.
Stage 5: Frontend Project โ Build a simple project.
Stage 6: Python (Core + OOP) โ Learn Python fundamentals.
Stage 7: Backend Project โ Use Flask/Django for backend.
Stage 8: Frameworks โ Master Flask/Django features.
Stage 1: HTML โ Learn webpage basics.
Stage 2: CSS โ Style web pages.
Stage 3: JavaScript โ Add interactivity.
Stage 4: Git + GitHub โ Manage code versions.
Stage 5: Frontend Project โ Build a simple project.
Stage 6: Python (Core + OOP) โ Learn Python fundamentals.
Stage 7: Backend Project โ Use Flask/Django for backend.
Stage 8: Frameworks โ Master Flask/Django features.
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