Coding Free Books | Python | AI

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948 views05:53

Coding Free Books | Python | AI

Top 10 CSS Interview Questions

1. What is CSS and what are its key features?
CSS (Cascading Style Sheets) is a stylesheet language used to describe the presentation of a document written in HTML or XML. Its key features include controlling layout, styling text, setting colors, spacing, and more, allowing for a separation of content and design for better maintainability and flexibility.

2. Explain the difference between inline, internal, and external CSS.
- Inline CSS is applied directly within an HTML element using the style attribute.
- Internal CSS is defined within a <style> tag inside the <head> section of an HTML document.
- External CSS is linked to an HTML document via the <link> tag and is written in a separate .css file.

3. What is the CSS box model and what are its components?
The CSS box model describes the rectangular boxes generated for elements in the document tree and consists of four components:
- Content: The actual content of the element.
- Padding: The space between the content and the border.
- Border: The edge surrounding the padding.
- Margin: The space outside the border that separates the element from others.

4. How do you center a block element horizontally using CSS?
To center a block element horizontally, you can use the margin: auto; property. For example:

.center {
  width: 50%;
  margin: auto;
}

5. What are CSS selectors and what are the different types?
CSS selectors are patterns used to select elements to apply styles. The different types include:
- Universal selector (*)
- Element selector (element)
- Class selector (.class)
- ID selector (#id)
- Attribute selector ([attribute])
- Pseudo-class selector (:pseudo-class)
- Pseudo-element selector (::pseudo-element)

6. Explain the difference between absolute, relative, fixed, and sticky positioning in CSS.
- relative: The element is positioned relative to its normal position.
- absolute: The element is positioned relative to its nearest positioned ancestor or the initial containing block if none exists.
- fixed: The element is positioned relative to the viewport and does not move when the page is scrolled.
- sticky: The element is treated as relative until a given offset position is met in the viewport, then it behaves as fixed.

7. What is Flexbox and how is it used in CSS?
Flexbox (Flexible Box Layout) is a layout model that allows for more efficient arrangement of elements within a container. It is used to align and distribute space among items in a container, even when their size is unknown or dynamic. Flexbox is enabled by setting display: flex; on a container element.

8. How do you create a responsive design in CSS?
Responsive design can be achieved using media queries, flexible grid layouts, and relative units like percentages, em, and rem. Media queries adjust styles based on the viewport's width, height, and other characteristics. For example:

@media (max-width: 600px) {
  .container {
    width: 100%;
  }
}

9. What are CSS preprocessors and name a few popular ones.
CSS preprocessors extend CSS with variables, nested rules, and functions, making it more powerful and easier to maintain. Popular CSS preprocessors include:
- Sass (Syntactically Awesome Style Sheets)
- LESS (Leaner Style Sheets)
- Stylus

10. How do you implement CSS animations?
CSS animations are implemented using the @keyframes rule to define the animation and the animation property to apply it to an element. For example:

@keyframes example {
  from {background-color: red;}
  to {background-color: yellow;}
}

.element {
  animation: example 5s infinite;
}

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1.06K views17:21

Coding Free Books | Python | AI

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Coding Free Books | Python | AI

You can learn ReactJS easily 🤩

Here's all you need to get started 🙌

1.Components
• Functional Components
• Class Components
• JSX (JavaScript XML) Syntax

2.Props (Properties)
• Passing Props
• Default Props
• Prop Types

3.State
• useState Hook
• Class Component State
• Immutable State

4.Lifecycle Methods (Class Components)
• componentDidMount
• componentDidUpdate
• componentWillUnmount

5.Hooks (Functional Components)
• useState
• useEffect
• useContext
• useReducer
• useCallback
• useMemo
• useRef
• useImperativeHandle
• useLayoutEffect

6.Event Handling
• Handling Events in Functional Components
• Handling Events in Class Components

7.Conditional Rendering
• if Statements
• Ternary Operators
• Logical && Operator

8.Lists and Keys
• Rendering Lists
• Keys in React Lists

9.Component Composition
• Reusing Components
• Children Props
• Composition vs Inheritance

10.Higher-Order Components (HOC)
• Creating HOCs
• Using HOCs for Reusability

11.Render Props
• Using Render Props Pattern

12.React Router
• <BrowserRouter>
• <Route>
• <Link>
• <Switch>
• Route Parameters

13.Navigation
• useHistory Hook
• useLocation Hook

State Management

14.Context API
• Creating Context
• useContext Hook

15.Redux
• Actions
• Reducers
• Store
• connect Function (React-Redux)

16.Forms
• Handling Form Data
• Controlled Components
• Uncontrolled Components

17.Side Effects
• useEffect for Data Fetching
• useEffect Cleanup

18.AJAX Requests
• Fetch API
• Axios Library

Error Handling

19.Error Boundaries
• componentDidCatch (Class Components)
• ErrorBoundary Component (Functional
Components)

20.Testing
• Jest Testing Framework
• React Testing Library

21. Best Practices
• Code Splitting
• PureComponent and React.memo
• Avoiding Reconciliation
• Keys for Dynamic Lists

22.Optimization
• Memoization
• Profiling and Performance Monitoring

23. Build and Deployment
• Create React App (CRA)
• Production Builds
• Deployment Strategies

Frameworks and Libraries

24.Styling Libraries
• Styled-components
• CSS Modules

25.State Management Libraries
• Redux
• MobX

26.Routing Libraries
• React Router
• Reach Router

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Coding Free Books | Python | AI

Forwarded from Python for Data Analysts

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Coding Free Books | Python | AI

Python Project Ideas

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1.18K views13:31

Coding Free Books | Python | AI

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1.25K views08:49

Coding Free Books | Python | AI

free resources for HTML, CSS, and JavaScript:

1. Documentation and Tutorials:
- [MDN Web Docs](https://developer.mozilla.org/en-US/)
- [W3Schools](https://www.w3schools.com/)

2. Interactive Learning:
- [Codecademy](https://www.codecademy.com/)
- [freeCodeCamp](https://www.freecodecamp.org/)

3. Web Design Community:
- [CSS-Tricks](https://css-tricks.com/)

4. Open Source Projects:
- [GitHub](https://github.com/)

5. Problem-solving:
- [Stack Overflow](https://stackoverflow.com/)

6. Images for Projects:
- [Unsplash](https://unsplash.com/)
- [Pexels](https://www.pexels.com/)

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1.2K views08:07

Coding Free Books | Python | AI

Coding is just like the language we use to talk to computers. It's not the skill itself, but rather how do I innovate? How do I build something interesting for my end users?

In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.

Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.

Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.

Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.

Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.

I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.

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1.21K views10:56

Coding Free Books | Python | AI

Important questions to ace your machine learning interview with an approach to answer:

1. Machine Learning Project Lifecycle:
   - Define the problem
   - Gather and preprocess data
   - Choose a model and train it
   - Evaluate model performance
   - Tune and optimize the model
   - Deploy and maintain the model

2. Supervised vs Unsupervised Learning:
   - Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
   - Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).

3. Evaluation Metrics for Regression:
   - Mean Absolute Error (MAE)
   - Mean Squared Error (MSE)
   - Root Mean Squared Error (RMSE)
   - R-squared (coefficient of determination)

4. Overfitting and Prevention:
   - Overfitting: Model learns the noise instead of the underlying pattern.
   - Prevention: Use simpler models, cross-validation, regularization.

5. Bias-Variance Tradeoff:
   - Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.

6. Cross-Validation:
   - Technique to assess model performance by splitting data into multiple subsets for training and validation.

7. Feature Selection Techniques:
   - Filter methods (e.g., correlation analysis)
   - Wrapper methods (e.g., recursive feature elimination)
   - Embedded methods (e.g., Lasso regularization)

8. Assumptions of Linear Regression:
   - Linearity
   - Independence of errors
   - Homoscedasticity (constant variance)
   - No multicollinearity

9. Regularization in Linear Models:
   - Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.

10. Classification vs Regression:
    - Classification: Predicts a categorical outcome (e.g., class labels).
    - Regression: Predicts a continuous numerical outcome (e.g., house price).

11. Dimensionality Reduction Algorithms:
    - Principal Component Analysis (PCA)
    - t-Distributed Stochastic Neighbor Embedding (t-SNE)

12. Decision Tree:
    - Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.

13. Ensemble Methods:
    - Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).

14. Handling Missing or Corrupted Data:
    - Imputation (e.g., mean substitution)
    - Removing rows or columns with missing data
    - Using algorithms robust to missing values

15. Kernels in Support Vector Machines (SVM):
    - Linear kernel
    - Polynomial kernel
    - Radial Basis Function (RBF) kernel

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1.15K views11:17

Coding Free Books | Python | AI

Vuejs complete guide.pdf

6.1 MB

🧿 The Ultimate Vue.js Guide Pdf ✅

One of the most frameworks used for frontend 🔰

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1.21K views12:45

Coding Free Books | Python | AI