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โค3๐1
You can learn ReactJS easily ๐คฉ
Here's all you need to get started ๐
1.Components
โข Functional Components
โข Class Components
โข JSX (JavaScript XML) Syntax
2.Props (Properties)
โข Passing Props
โข Default Props
โข Prop Types
3.State
โข useState Hook
โข Class Component State
โข Immutable State
4.Lifecycle Methods (Class Components)
โข componentDidMount
โข componentDidUpdate
โข componentWillUnmount
5.Hooks (Functional Components)
โข useState
โข useEffect
โข useContext
โข useReducer
โข useCallback
โข useMemo
โข useRef
โข useImperativeHandle
โข useLayoutEffect
6.Event Handling
โข Handling Events in Functional Components
โข Handling Events in Class Components
7.Conditional Rendering
โข if Statements
โข Ternary Operators
โข Logical && Operator
8.Lists and Keys
โข Rendering Lists
โข Keys in React Lists
9.Component Composition
โข Reusing Components
โข Children Props
โข Composition vs Inheritance
10.Higher-Order Components (HOC)
โข Creating HOCs
โข Using HOCs for Reusability
11.Render Props
โข Using Render Props Pattern
12.React Router
โข <BrowserRouter>
โข <Route>
โข <Link>
โข <Switch>
โข Route Parameters
13.Navigation
โข useHistory Hook
โข useLocation Hook
State Management
14.Context API
โข Creating Context
โข useContext Hook
15.Redux
โข Actions
โข Reducers
โข Store
โข connect Function (React-Redux)
16.Forms
โข Handling Form Data
โข Controlled Components
โข Uncontrolled Components
17.Side Effects
โข useEffect for Data Fetching
โข useEffect Cleanup
18.AJAX Requests
โข Fetch API
โข Axios Library
Error Handling
19.Error Boundaries
โข componentDidCatch (Class Components)
โข ErrorBoundary Component (Functional
Components)
20.Testing
โข Jest Testing Framework
โข React Testing Library
21. Best Practices
โข Code Splitting
โข PureComponent and React.memo
โข Avoiding Reconciliation
โข Keys for Dynamic Lists
22.Optimization
โข Memoization
โข Profiling and Performance Monitoring
23. Build and Deployment
โข Create React App (CRA)
โข Production Builds
โข Deployment Strategies
Frameworks and Libraries
24.Styling Libraries
โข Styled-components
โข CSS Modules
25.State Management Libraries
โข Redux
โข MobX
26.Routing Libraries
โข React Router
โข Reach Router
React โค๏ธ for more
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Here's all you need to get started ๐
1.Components
โข Functional Components
โข Class Components
โข JSX (JavaScript XML) Syntax
2.Props (Properties)
โข Passing Props
โข Default Props
โข Prop Types
3.State
โข useState Hook
โข Class Component State
โข Immutable State
4.Lifecycle Methods (Class Components)
โข componentDidMount
โข componentDidUpdate
โข componentWillUnmount
5.Hooks (Functional Components)
โข useState
โข useEffect
โข useContext
โข useReducer
โข useCallback
โข useMemo
โข useRef
โข useImperativeHandle
โข useLayoutEffect
6.Event Handling
โข Handling Events in Functional Components
โข Handling Events in Class Components
7.Conditional Rendering
โข if Statements
โข Ternary Operators
โข Logical && Operator
8.Lists and Keys
โข Rendering Lists
โข Keys in React Lists
9.Component Composition
โข Reusing Components
โข Children Props
โข Composition vs Inheritance
10.Higher-Order Components (HOC)
โข Creating HOCs
โข Using HOCs for Reusability
11.Render Props
โข Using Render Props Pattern
12.React Router
โข <BrowserRouter>
โข <Route>
โข <Link>
โข <Switch>
โข Route Parameters
13.Navigation
โข useHistory Hook
โข useLocation Hook
State Management
14.Context API
โข Creating Context
โข useContext Hook
15.Redux
โข Actions
โข Reducers
โข Store
โข connect Function (React-Redux)
16.Forms
โข Handling Form Data
โข Controlled Components
โข Uncontrolled Components
17.Side Effects
โข useEffect for Data Fetching
โข useEffect Cleanup
18.AJAX Requests
โข Fetch API
โข Axios Library
Error Handling
19.Error Boundaries
โข componentDidCatch (Class Components)
โข ErrorBoundary Component (Functional
Components)
20.Testing
โข Jest Testing Framework
โข React Testing Library
21. Best Practices
โข Code Splitting
โข PureComponent and React.memo
โข Avoiding Reconciliation
โข Keys for Dynamic Lists
22.Optimization
โข Memoization
โข Profiling and Performance Monitoring
23. Build and Deployment
โข Create React App (CRA)
โข Production Builds
โข Deployment Strategies
Frameworks and Libraries
24.Styling Libraries
โข Styled-components
โข CSS Modules
25.State Management Libraries
โข Redux
โข MobX
26.Routing Libraries
โข React Router
โข Reach Router
React โค๏ธ for more
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โค6
Forwarded from Python for Data Analysts
๐ช๐ฎ๐ป๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ง๐ต๐ฎ๐ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐๐ฟ๐ฒ ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ผ๐ฟ?๐
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If youโre looking to land a job in tech or simply want to upskill without spending money, this is your golden chanceโจ๏ธ๐
Weโve handpicked 5 YouTube channels that teach 5 in-demand tech skills for FREE. These skills are widely sought after by employers in 2025 โ from startups to top MNCs๐งโ๐ป
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โค1
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โค3
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/)
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/)
โค3
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.
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.
โค7
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
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
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
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
โค2๐ฅ1
Vuejs complete guide.pdf
6.1 MB
๐งฟ The Ultimate Vue.js Guide Pdf โ
One of the most frameworks used for frontend ๐ฐ
React โค๏ธ and like my posts above ๐
One of the most frameworks used for frontend ๐ฐ
React โค๏ธ and like my posts above ๐
โค2
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