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Post #2231
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Important Machine Learning Algorithms 👆
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Showing posts older than #2239 · Back to latest
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Post #2224
2.26K
Java Learning Plan ✅
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Post #2222
2.25K
😂😂
- 😁 21
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Post #2221
2.09K
Python Cheatsheet
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Post #2219
2.22K
Here are 20 essential VS Code shortcuts for beginners:
1. Ctrl + P: Open any file quickly 📂
2. Ctrl + /: Toggle line comment 📝
3. Alt + Up/Down: Move a line up or down ↕️
4. Ctrl + Shift + K: Delete the current line ❌
5. Ctrl + B: Show/hide the sidebar 📚
6. Ctrl + Space: Trigger IntelliSense for code suggestions 💡
7. Ctrl + Shift + F: Search across files 🔍
8. Ctrl + D: Select the next occurrence of the selected text 📑
9. Ctrl + Shift + L: Select all occurrences of the current selection 🔗
10. Ctrl + Shift + P: Open the Command Palette 📜
11. Ctrl + F2: Rename all occurrences of a variable ✏️
12. Ctrl + J: Show/hide the integrated terminal 💻
13. Ctrl + `: Open a new terminal 🔧
14. Ctrl + Shift + N: Open a new window 🖼️
15. Ctrl + W: Close the current editor tab 🗂️
16. Ctrl + Shift + E: Focus on the file explorer 🗃️
17. Ctrl + Shift + G: Open the Git view 🔄
18. Ctrl + Shift + M: Open the Problems panel 🚨
19. Alt + Shift + Up/Down: Copy the line up or down 📋
20. Ctrl + Alt + Arrow keys: Split the editor window ✂️
Master these and level up your coding speed! 🚀
1. Ctrl + P: Open any file quickly 📂
2. Ctrl + /: Toggle line comment 📝
3. Alt + Up/Down: Move a line up or down ↕️
4. Ctrl + Shift + K: Delete the current line ❌
5. Ctrl + B: Show/hide the sidebar 📚
6. Ctrl + Space: Trigger IntelliSense for code suggestions 💡
7. Ctrl + Shift + F: Search across files 🔍
8. Ctrl + D: Select the next occurrence of the selected text 📑
9. Ctrl + Shift + L: Select all occurrences of the current selection 🔗
10. Ctrl + Shift + P: Open the Command Palette 📜
11. Ctrl + F2: Rename all occurrences of a variable ✏️
12. Ctrl + J: Show/hide the integrated terminal 💻
13. Ctrl + `: Open a new terminal 🔧
14. Ctrl + Shift + N: Open a new window 🖼️
15. Ctrl + W: Close the current editor tab 🗂️
16. Ctrl + Shift + E: Focus on the file explorer 🗃️
17. Ctrl + Shift + G: Open the Git view 🔄
18. Ctrl + Shift + M: Open the Problems panel 🚨
19. Alt + Shift + Up/Down: Copy the line up or down 📋
20. Ctrl + Alt + Arrow keys: Split the editor window ✂️
Master these and level up your coding speed! 🚀
- 👍 6
Post #2214
2.36K
Python libraries for data science and Machine Learning 👇👇
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation and analysis library that provides data structures like DataFrames and Series, making it easy to work with structured data.
3. Matplotlib: Matplotlib is a plotting library that enables the creation of various types of visualizations, such as line plots, bar charts, histograms, scatter plots, etc., to explore and communicate data effectively.
4. Scikit-learn: Scikit-learn is a machine learning library that offers a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. It also provides tools for model selection and evaluation.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google that is widely used for building deep learning models. It provides a comprehensive ecosystem of tools and libraries for developing and deploying machine learning applications.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It simplifies the process of building and training deep learning models by providing a user-friendly interface.
7. SciPy: SciPy is a scientific computing library that builds on top of NumPy and provides additional functionality for optimization, integration, interpolation, linear algebra, signal processing, and more.
8. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a higher-level interface for creating attractive and informative statistical graphics.
Channel credits: https://t.me/datasciencefun
ENJOY LEARNING 👍👍
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation and analysis library that provides data structures like DataFrames and Series, making it easy to work with structured data.
3. Matplotlib: Matplotlib is a plotting library that enables the creation of various types of visualizations, such as line plots, bar charts, histograms, scatter plots, etc., to explore and communicate data effectively.
4. Scikit-learn: Scikit-learn is a machine learning library that offers a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. It also provides tools for model selection and evaluation.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google that is widely used for building deep learning models. It provides a comprehensive ecosystem of tools and libraries for developing and deploying machine learning applications.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It simplifies the process of building and training deep learning models by providing a user-friendly interface.
7. SciPy: SciPy is a scientific computing library that builds on top of NumPy and provides additional functionality for optimization, integration, interpolation, linear algebra, signal processing, and more.
8. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a higher-level interface for creating attractive and informative statistical graphics.
Channel credits: https://t.me/datasciencefun
ENJOY LEARNING 👍👍
- 👍 4
- ❤ 1
Post #2207
2.13K
Types of API ✅
- 👍 2