ML and Thirukkuarl - 2024 Perspective
Main concepts of Machine Learning:
1. Supervised Learning : Training a model on labeled data, where each input is paired with a corresponding target output.
2. Unsupervised Learning : Training a model on unlabeled data to learn patterns and structures without explicit supervision.
3. Semi-Supervised Learning : Training a model on a combination of labeled and unlabeled data to improve performance.
4. Reinforcement Learning : Training a model to make sequences of decisions by rewarding desired behavior and penalizing undesired behavior.
5. Classification : Predicting categories or labels for new data points based on past observations.
6. Regression : Predicting continuous outcomes or values based on input features.
7. Clustering : Grouping similar data points together based on their characteristics or features.
8. Dimensionality Reduction : Reducing the number of input variables or features in a dataset while retaining important information.
9. Feature Engineering : Creating new features or transforming existing ones to improve model performance.
10. Model Evaluation and Validation : Assessing the performance of a model using metrics such as accuracy, precision, recall, and F1 score.
11. Cross-Validation : Splitting data into multiple subsets to train and test the model on different combinations of data.
12. Bias-Variance Tradeoff : Balancing the complexity of a model to minimize both bias (underfitting) and variance (overfitting).
13. Hyperparameter Tuning : Optimizing the parameters of a model to improve performance and generalization.
14. Ensemble Learning : Combining multiple models to improve predictive performance, such as bagging, boosting, and stacking.
15. Deep Learning : Training neural networks with multiple layers to automatically learn hierarchical representations of data.
16. Convolutional Neural Networks (CNNs) : Deep learning models specifically designed for processing structured grid-like data, such as images.
17. Recurrent Neural Networks (RNNs) : Deep learning models designed for sequential data processing, such as time series or natural language.
18. Generative Adversarial Networks (GANs) : Deep learning models consisting of two neural networks (generator and discriminator) trained adversarially to generate realistic data samples.
19. Natural Language Processing (NLP) : Processing and analyzing human language data using machine learning techniques.
20. Transfer Learning : Leveraging pre-trained models on similar tasks to improve performance on a new task with limited data.
Look below, it is amazing, Thiruvalluvar, immortal poet, he scribed before 2000 years itself. He explained this concepts. Let us see one by one.
Machine Learning Concepts Explained through Thirukkural😂😂😂😂
| ML Concept | Explanation | Thirukkural Verse begin with | Interpretation |
|---|---|---|---|
| Supervised Learning | Learning from labeled data provided by an expert. | "கல்வி யார்க்கும் கழியாதார்..." | Just as a disciple learns from a knowledgeable guru. |
| Unsupervised Learning | Learning from unlabeled data through observation. | "கற்றதனால் ஆய பயனென்கொல்..." | Similar to unsupervised learning, where knowledge gained through self-experience is invaluable. |
| Reinforcement Learning | Learning from rewards and punishments. | "புறங்கொளி பூசியார் அறியார்..." | Similar to individuals learning from rewards and punishments. |
| Classification | Categorizing data into predefined classes. | "அறன்மேல் வாழாத உலகு" | Like classification, where objects are categorized into different classes based on their attributes. |
| Regression | Predicting continuous outcomes based on input data. | "நெஞ்சுற்றல் நீர உழவு" | Similar to regression, which predicts continuous outcomes. |
| Clustering | Grouping similar data points together. | "தொடர்ச்சி வாழ்வாங்கு வாழ்பவன் மன்னன்..." | Like clustering, where individuals with common traits are grouped together. |
| Dimensionality Reduction | Reducing the number of input variables. | "சிறப்பு தெரிந்து சிறப்பினை..." | Similar to reducing the complexity of data in dimensionality reduction. |
| Feature Engineering | Creating new features or transforming existing ones. | "பிறவா ழைபூவா கண்ணும் அனையா..." | Like feature engineering, which enhances the predictive power of models. |
| Model Evaluation and Validation | Assessing the performance of a model. | "செய்தவழி தீய அறியா..." | Similar to evaluating models for performance. |
| Cross-Validation | Splitting data into subsets for testing and training. | "ஒற்றின் விழையென்று வேல்வீழும்..." | Like cross-validation, which tests models on diverse subsets of data. |
| Bias-Variance Tradeoff | Balancing model complexity for optimal performance. | "அறனுடைய அளவும் உணர்வு..." | Just as balancing righteousness and knowledge leads to harmony. |
| Hyperparameter Tuning | Optimizing parameters to improve model performance. | "அறத்துப்பால் அரண் மடியின்..." | Similar to fine-tuning hyperparameters to optimize model performance. |
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