➡️ *Architecture:* Uses a compound scaling method to uniformly scale network depth, width, and resolution for optimal performance.
➡️ *Strengths:* Achieves high accuracy with fewer parameters and computational cost.
➡️ *Applications* : Image classification, object detection, and tasks requiring efficient resource usage.
➡️ *Limitations* : Requires careful tuning of scaling parameters.

7️⃣ *NASNet*

➡️ *Architecture:* Neural Architecture Search Network (NASNet) is designed using automated neural architecture search (NAS) to optimize the model architecture for a specific task.
➡️ *Strengths:* State-of-the-art performance, highly optimized for specific tasks.
➡️ *Applications:* Image classification, object detection, and segmentation.
➡️ *Limitations:* Computationally expensive to design, complex architecture.

❄️ _*Comparison of Models by Application*_

💥 *Summary*

1️⃣ *VGG16:* Best for feature extraction and simple tasks.
2️⃣ *MobileNet* : Ideal for mobile and edge devices.
3️⃣ *DenseNet:* Suitable for tasks requiring feature reuse and efficiency.
4️⃣ *Inception* : General-purpose, efficient multi-scale feature extraction.
5️⃣ *ResNet:* High-accuracy tasks with deep networks.
6️⃣ *EfficientNet:* Best balance of accuracy and efficiency.
7️⃣ *NASNet:* State-of-the-art performance for specific tasks.

_*Each model has its strengths and is suited to different applications depending on the requirements for accuracy, speed, and resource usage. Transfer learning allows these models to be adapted to new tasks efficiently, making them invaluable in modern deep learning workflows.*

Transfer Learning

CNN Transfer Learning

CNN Transfer Learning Python Code

CNN Transfer Learning image Dataset

1. What is Transfer Learning?

Answer: Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second, related task.

2. Why is Transfer Learning useful?

Answer: It reduces the need for large datasets and computational resources, speeds up training, and improves performance, especially when data for the target task is limited.

3. Name one common application of Transfer Learning.

Answer: Image classification (e.g., using a pre-trained model like ResNet or VGG for a new image dataset).

4. What is a pre-trained model?

Answer: A pre-trained model is a model that has already been trained on a large dataset (e.g., ImageNet) and can be fine-tuned or used as a feature extractor for a new task.

5. What are the two main approaches to Transfer Learning?

Answer:
1. Feature Extraction: Using the pre-trained model as a fixed feature extractor.
2. Fine-Tuning: Further training the pre-trained model on the new task.

6. What is the difference between fine-tuning and feature extraction?

Answer:
• Feature Extraction: Only the final layers (e.g., classifier) are trained, while the pre-trained layers remain frozen.
• Fine-Tuning: Some or all layers of the pre-trained model are further trained on the new dataset.

7. What is a common dataset used for pre-training models in computer vision?

Answer: ImageNet.

8. What is the main challenge in Transfer Learning?

Answer: Ensuring the source and target tasks are sufficiently related for the knowledge transfer to be effective.

9. What is domain adaptation in Transfer Learning?

Answer: Domain adaptation is a subfield of transfer learning where the source and target domains are different but related (e.g., different data distributions).

10. Name one popular pre-trained model used in NLP.

Answer: BERT (Bidirectional Encoder Representations from Transformers).

11. What is the purpose of freezing layers in Transfer Learning?

Answer: Freezing layers prevents them from being updated during training, preserving the learned features from the pre-trained model.

12. What is the risk of overfitting in Transfer Learning?

Answer: Overfitting can occur if the new dataset is too small, causing the model to memorize the data instead of generalizing.

13. What is the role of the learning rate in fine-tuning?

Answer: A smaller learning rate is typically used to avoid overwriting the pre-trained weights too quickly.

14. What is the difference between inductive and transductive Transfer Learning?

Answer:
• Inductive Transfer Learning: The source and target tasks are different.
•Transductive Transfer Learning: The source and target tasks are the same, but the domains are different.

15. What is multi-task learning, and how is it related to Transfer Learning?

Answer: Multi-task learning involves training a model on multiple related tasks simultaneously, sharing knowledge between tasks. It is related to transfer learning as both involve leveraging knowledge from one task to improve performance on another.

📚 NLP Transformer-Based Models for Sentiment Analysis on Twitter Data with Code

Sentiment analysis is a common Natural Language Processing (NLP) task that involves determining the emotional tone or polarity (positive, negative, or neutral) of a given text. Transformer-based models have revolutionized NLP by providing state-of-the-art performance across various tasks, including sentiment analysis. Below is an explanation of the Transformer-based models you mentioned and how they are used for sentiment analysis:

1️⃣ BERT (Bidirectional Encoder Representations from Transformers)

Overview: BERT is a Transformer-based model introduced by Google in 2018. It uses a bidirectional architecture, meaning it processes text in both directions (left-to-right and right-to-left) simultaneously. This allows BERT to capture context from both sides of a word, making it highly effective for understanding the meaning of words in context.

📕 Key Features:
o Pretrained on large corpora using Masked Language Modeling (MLM) and Next Sentence Prediction (NSP).
o Fine-tuned on downstream tasks like sentiment analysis.

📇 Use in Sentiment Analysis:
o BERT can be fine-tuned on labeled sentiment datasets (e.g., IMDb, Yelp reviews) to classify text into positive, negative, or neutral sentiments.
o Its bidirectional nature helps it understand nuanced sentiments in complex sentences.

2️⃣ RoBERTa (Robustly Optimized BERT Approach)

📇 Overview: RoBERTa is an optimized version of BERT developed by Facebook AI. It removes the Next Sentence Prediction (NSP) objective and introduces dynamic masking during training, leading to better performance.

📕 Key Features:
o Trained on larger datasets and for more iterations compared to BERT.
o More robust and generalizable due to improved pretraining techniques.

📇 Use in Sentiment Analysis:
o RoBERTa is fine-tuned on sentiment analysis datasets, often outperforming BERT due to its optimized training process.
o It is particularly effective for handling longer texts and complex sentiment expressions.

3️⃣ DistilBERT

• Overview: DistilBERT is a smaller, faster, and lighter version of BERT developed by Hugging Face. It retains 95% of BERT's performance while being 40% smaller and 60% faster.

📕 Key Features:
o Uses knowledge distillation during training, where a smaller model (DistilBERT) learns from a larger model (BERT).
o Ideal for applications with limited computational resources.

📇 Use in Sentiment Analysis:
o DistilBERT is fine-tuned for sentiment analysis tasks, providing a good balance between accuracy and efficiency.
o Suitable for real-time sentiment analysis applications.

4️⃣ ALBERT (A Lite BERT)

• Overview: ALBERT is a lightweight version of BERT designed to reduce the number of parameters while maintaining performance. It introduces two key innovations: factorized embedding parameterization and cross-layer parameter sharing.

📕 Key Features:
o Reduces memory usage and improves training speed.
o Achieves comparable or better performance than BERT on many NLP tasks.

📇 Use in Sentiment Analysis:
o ALBERT is fine-tuned on sentiment datasets, offering a more efficient alternative to BERT.
o Its reduced parameter size makes it suitable for deployment in resource-constrained environments.

5️⃣ XLNet

Overview: XLNet is a generalized autoregressive pretraining model that overcomes the limitations of BERT's masked language modeling. It uses a permutation-based approach to capture bidirectional context.

📕 Key Features:
o Combines the strengths of autoregressive models (like GPT) and autoencoding models (like BERT).
o Pretrained using a permutation language modeling objective, which allows it to consider all possible word orders.

📇 Use in Sentiment Analysis:
o XLNet is fine-tuned for sentiment analysis tasks and often achieves state-of-the-art results.
o Its ability to model long-range dependencies makes it effective for analyzing sentiments in long texts.

Summary of Use in Sentiment Analysis:

• BERT: Baseline model for sentiment analysis, bidirectional context understanding.
• RoBERTa: Optimized version of BERT, better performance on larger datasets.
• DistilBERT: Lightweight and efficient, suitable for real-time applications.
• ALBERT: Reduced parameter size, efficient for resource-constrained environments.
• XLNet: State-of-the-art performance, effective for long-range dependencies.

NLP Transformer-based Models used for Sentiment Analysis

NLP Transformer-based Models used for Sentiment Analysis (Python Code)

NLP Transformer-based Models used for Sentiment Analysis (Twitter Data)

📚 Data Science and Machine Learning Algorithms

Day 1: Linear Regression
- Concept: Predict continuous values.
- Implementation: Ordinary Least Squares.
- Evaluation: R-squared, RMSE.

Day 2: Logistic Regression
- Concept: Binary classification.
- Implementation: Sigmoid function.
- Evaluation: Confusion matrix, ROC-AUC.

Day 3: Decision Trees
- Concept: Tree-based model for classification/regression.
- Implementation: Recursive splitting.
- Evaluation: Accuracy, Gini impurity.

Day 4: Random Forest
- Concept: Ensemble of decision trees.
- Implementation: Bagging.
- Evaluation: Out-of-bag error, feature importance.

Day 5: Gradient Boosting
- Concept: Sequential ensemble method.
- Implementation: Boosting.
- Evaluation: Learning rate, number of estimators.

Day 6: Support Vector Machines (SVM)
- Concept: Classification using hyperplanes.
- Implementation: Kernel trick.
- Evaluation: Margin maximization, support vectors.

Day 7: k-Nearest Neighbors (k-NN)
- Concept: Instance-based learning.
- Implementation: Distance metrics.
- Evaluation: k-value tuning, distance functions.

Day 8: Naive Bayes
- Concept: Probabilistic classifier.
- Implementation: Bayes' theorem.
- Evaluation: Prior probabilities, likelihood.

Day 9: k-Means Clustering
- Concept: Partitioning data into k clusters.
- Implementation: Centroid initialization.
- Evaluation: Inertia, silhouette score.

Day 10: Hierarchical Clustering
- Concept: Nested clusters.
- Implementation: Agglomerative method.
- Evaluation: Dendrograms, linkage methods.

Day 11: Principal Component Analysis (PCA)
- Concept: Dimensionality reduction.
- Implementation: Eigenvectors, eigenvalues.
- Evaluation: Explained variance.

Day 12: Association Rule Learning
- Concept: Discover relationships between variables.
- Implementation: Apriori algorithm.
- Evaluation: Support, confidence, lift.

Day 13: DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Concept: Density-based clustering.
- Implementation: Epsilon, min samples.
- Evaluation: Core points, noise points.

Day 14: Linear Discriminant Analysis (LDA)
- Concept: Linear combination for classification.
- Implementation: Fisher's criterion.
- Evaluation: Class separability.

Day 15: XGBoost
- Concept: Extreme Gradient Boosting.
- Implementation: Tree boosting.
- Evaluation: Regularization, parallel processing.

Day 16: LightGBM
- Concept: Gradient boosting framework.
- Implementation: Leaf-wise growth.
- Evaluation: Speed, accuracy.

Day 17: CatBoost
- Concept: Gradient boosting with categorical features.
- Implementation: Ordered boosting.
- Evaluation: Handling of categorical data.

Day 18: Neural Networks
- Concept: Layers of neurons for learning.
- Implementation: Backpropagation.
- Evaluation: Activation functions, epochs.

Day 19: Convolutional Neural Networks (CNNs)
- Concept: Image processing.
- Implementation: Convolutions, pooling.
- Evaluation: Feature maps, filters.

Day 20: Recurrent Neural Networks (RNNs)
- Concept: Sequential data processing.
- Implementation: Hidden states.
- Evaluation: Long-term dependencies.

Day 21: Long Short-Term Memory (LSTM)
- Concept: Improved RNN.
- Implementation: Memory cells.
- Evaluation: Forget gates, output gates.

Day 22: Gated Recurrent Units (GRU)
- Concept: Simplified LSTM.
- Implementation: Update gate.
- Evaluation: Performance, complexity.

Day 23: Autoencoders
- Concept: Data compression.
- Implementation: Encoder, decoder.
- Evaluation: Reconstruction error.

Day 24: Generative Adversarial Networks (GANs)
- Concept: Generative models.
- Implementation: Generator, discriminator.
- Evaluation: Adversarial loss.

Day 25: Transfer Learning
- Concept: Pre-trained models.
- Implementation: Fine-tuning.
- Evaluation: Domain adaptation.

Day 26: Reinforcement Learning
- Concept: Learning through interaction.
- Implementation: Q-learning.
- Evaluation: Reward function, policy.

Day 27: Bayesian Networks
- Concept: Probabilistic graphical models.
- Implementation: Conditional dependencies.
- Evaluation: Inference, learning.

Day 28: Hidden Markov Models (HMM)
- Concept: Time series analysis.
- Implementation: Transition probabilities.
- Evaluation: Viterbi algorithm.

Day 29: Feature Selection Techniques
- Concept: Improving model performance.
- Implementation: Filter, wrapper methods.
- Evaluation: Feature importance.

Day 30: Hyperparameter Optimization
- Concept: Model tuning.
- Implementation: Grid search, random search.
- Evaluation: Cross-validation.

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Key Concepts for Machine Learning Interviews

1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.

2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.

3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.

4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.

5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).

6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.

7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.

8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.

9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.

10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.

11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.

12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.

13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.

14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.

15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.

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*Hyperparameter Tuning in Machine Learning*

Hyperparameter tuning is a critical step in the machine learning (ML) pipeline, aimed at optimizing the performance of a model by selecting the best set of hyperparameters. Hyperparameters are configuration variables that govern the training process and model architecture, such as the learning rate, regularization strength, number of layers in a neural network, or the number of trees in a random forest. Unlike model parameters, which are learned during training, hyperparameters are set prior to training and require careful tuning to achieve optimal model performance.


1️⃣ *Define Hyperparameters*

*Objective:* Identify the hyperparameters that influence the model's performance.

*Examples:*

* Learning Rate: Controls the step size during gradient descent.
* Number of Layers/Neurons: Determines the architecture of a neural network.
* Regularization Parameters: Lambda (λ) for L1/L2 regularization to prevent overfitting.
* Batch Size: Number of samples processed before updating model weights.
* Number of Trees: In ensemble methods like Random Forest or Gradient Boosting.
* Kernel Type: In SVMs (e.g., linear, RBF, polynomial).

*Practical Tip* : Start with hyperparameters that have the most significant impact on model performance. For example, in deep learning, focus on learning rate, batch size, and network architecture.

2️⃣ *Choose a Search Space*

*Objective:* Define the range of values or options for each hyperparameter.

*Approaches:*


* Discrete Values: For categorical hyperparameters (e.g., kernel type in SVMs).
* Continuous Ranges: For numerical hyperparameters (e.g., learning rate between 0.0001 and 0.1).
* Logarithmic Scaling: For hyperparameters like learning rate or regularization strength, where values span multiple orders of magnitude (e.g., [0.0001, 0.001, 0.01, 0.1]).

*Practical Tip:* Use domain knowledge or literature to set reasonable bounds. For example, learning rates are often explored in the range [1e-5, 1e-1].


3️⃣ *Select an Evaluation Metric*

*Objective:* Choose a metric to evaluate model performance during tuning.

*Common Metrics:*

* Classification: Accuracy, Precision, Recall, F1 Score, AUC-ROC.
* Regression: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R².
* Custom Metrics: Task-specific metrics (e.g., Intersection over Union (IoU) for object detection).

*Practical Tip:* Align the metric with the business objective. For imbalanced datasets, prioritize metrics like F1 score or AUC-ROC over accuracy.

4️⃣ *Hyperparameter Optimization Methods*

Objective: Efficiently search the hyperparameter space to find the optimal configuration.

*Methods:*

*Grid Search:*
* Exhaustively evaluates all combinations of hyperparameters within the search space.
* Pros: Guarantees finding the best combination within the defined space.
* Cons: Computationally expensive, especially for high-dimensional spaces.
* Use Case: Small search spaces with few hyperparameters.
*Random Search:*
* Randomly samples hyperparameter combinations from the search space.
* Pros: More efficient than grid search for high-dimensional spaces.
* Cons: May miss the optimal combination.
* Use Case: Large search spaces where exhaustive search is infeasible.
*Bayesian Optimization:*
* Uses probabilistic models (e.g., Gaussian Processes) to predict the performance of hyperparameter configurations and focuses on promising regions.
* Pros: Efficient and effective for expensive-to-evaluate models.
* Cons: Requires more implementation effort.
* Use Case: Expensive models like deep neural networks.
*Evolutionary Algorithms:*
* Uses genetic algorithms to evolve hyperparameter configurations over generations.
* Use Case: Complex, non-convex search spaces.
*Hyperband:*
* A bandit-based approach that dynamically allocates resources to promising configurations.
* Use Case: Large-scale hyperparameter tuning with limited computational resources.

*Practical Tip* : Use libraries like Optuna, Hyperopt, or Scikit-Optimize for advanced optimization techniques.

5️⃣ *Cross-Validation for Robustness*

*Objective:* Ensure the model generalizes well to unseen data.

*Approach:*
* Split the dataset into k folds.
* Train the model on k-1 folds and validate on the remaining fold.
* Repeat for all folds and average the performance metric.
*Practical Tip:*
* Use stratified cross-validation for imbalanced datasets.
* For large datasets, a single validation set may suffice to reduce computational cost.


6️⃣ *Model Selection*

Objective: Select the best hyperparameter configuration based on the evaluation metric.

*Steps:*
* Train the final model on the entire training set using the best hyperparameters.
* Evaluate the model on a held-out test set to estimate generalization performance.

*Practical Tip:* Avoid overfitting to the validation set by using early stopping or regularization.


7️⃣ *Practical Considerations*

* Compute Resources: Hyperparameter tuning can be computationally expensive. Use distributed computing (e.g., Spark, Ray) or cloud-based solutions (e.g., AWS SageMaker, Google AI Platform) for scalability.

*Automation: Leverage automated hyperparameter tuning tools like:*
* Scikit-learn: GridSearchCV, RandomizedSearchCV.
* Keras Tuner: For deep learning models.
* Optuna: A flexible and efficient optimization framework.

*Reproducibility:* Set random seeds for reproducibility and log all hyperparameter configurations and results.


8️⃣ *Advanced Techniques*

* Transfer Learning: Use hyperparameters from similar tasks or models as a starting point.
* Meta-Learning: Leverage meta-models to predict good hyperparameter configurations based on dataset characteristics.
* Neural Architecture Search (NAS): Automatically discover optimal neural network architectures.

3️⃣3️⃣ Common Pre-processing steps commonly used before feeding data into an NLP model

1. Lowercasing
2. Tokenization
3. Removing Punctuation
4. Removing Stopwords
5. Stemming
6. Lemmatization
7. Removing Numbers
8. Removing Extra Spaces
9. Handling Contractions
10. Removing Special Characters
11. Part-of-Speech (POS) Tagging
12. Named Entity Recognition (NER)
13. Vectorization
14. Handling Missing Data
15. Normalization
16. Spelling Correction
17. Handling Emojis and Emoticons
18. Removing HTML Tags
19. Handling URLs
20. Handling Mentions and Hashtags
21. Sentence Segmentation
22. Handling Abbreviations
23. Language Detection
24. Text Encoding
25. Handling Whitespace Tokens
26. Handling Dates and Times
27. Text Augmentation
28. Handling Negations
29. Dependency Parsing
30. Handling Rare Words
31. Text Chunking
32. Handling Synonyms
33. Text Normalization for Social Media

The importance of preprocessing steps in NLP depends on the specific task, type of text data, and the NLP model being used. However, some steps are generally considered more critical across most NLP tasks. Here's a breakdown:

🔰 Most Important Preprocessing Steps for NLP

1️⃣Tokenization

➖ Why: Tokenization is the foundation of NLP. It breaks text into meaningful units (words, sentences, etc.), which are necessary for any further processing.
➖ When: Always required, regardless of the task.

2️⃣ Lowercasing

➖ Why: Ensures consistency by treating words like "Apple" and "apple" as the same. Reduces vocabulary size and computational complexity.
➖When: Important for tasks like text classification, sentiment analysis, and information retrieval.

3️⃣ Removing Stopwords

➖ Why: Stopwords (e.g., "the," "is," "and") add noise and don’t contribute much to the meaning in many tasks.
➖ When: Useful for tasks like text classification, topic modeling, and search engines.

4️⃣ Handling Missing Data

➖ Why: Incomplete or missing data can lead to poor model performance.
➖ When: Critical for all tasks, especially when working with real-world datasets.

5️⃣ Vectorization

➖ Why: Converts text into numerical representations (e.g., Bag of Words, TF-IDF, Word Embeddings) that machine learning models can process.
➖ When: Essential for all tasks involving machine learning or deep learning models.

6️⃣ Removing Punctuation and Special Characters

➖ Why: Punctuation and special characters often don’t contribute to the meaning and can add noise.
➖ When: Important for tasks like sentiment analysis, text classification, and machine translation.

7️⃣ Lemmatization or Stemming

➖ Why: Reduces words to their base forms, simplifying the vocabulary and improving consistency.
➖ When: Useful for tasks like information retrieval, text classification, and topic modeling.

8️⃣ Handling Contractions and Abbreviations

➖ Why: Expands contractions (e.g., "can't" → "cannot") and abbreviations (e.g., "ASAP" → "as soon as possible") for better understanding.
➖ When: Important for tasks involving informal text (e.g., social media analysis).

9️⃣ Handling URLs, Mentions, and Hashtags

➖ Why: Social media text often contains URLs, mentions (@user), and hashtags (#topic), which need to be processed or removed.
➖ When: Critical for social media text analysis.

🔟 Text Normalization

➖ Why: Standardizes text (e.g., converting dates, times, and numbers to a consistent format).
➖ When: Important for tasks involving structured data or time-sensitive analysis.

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33 Common Pre-processing steps commonly used before feeding data into an NLP model

Transformers for Natural Language Processing

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