List of Top 12 Coding Channels on WhatsApp:
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javanoscript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javanoscript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
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🧿 Essential React Hooks Part 1
Each hook addresses specific performance, state management, or accessibility needs within React components, allowing for efficient and organized code structure.
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🧿 Essential React Hooks Part 2
Each hook addresses specific performance, state management, or accessibility needs within React components, allowing for efficient and organized code structure.
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Machine Learning – Essential Concepts 🚀
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
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