💡 [FEATURE] - Adding proper sub topics for Machine Learning.

[karishmaaa101]

Idea Contribution

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Explain feature request

Adding proper sub-topics for Machine Learning like supervised learning, unsupervised learning, reinforcement learning , and deep learning.

Explain your solution

Supervised Learning: Supervised learning involves training a model on a labeled dataset, where each example consists of input data and the corresponding correct output. The model learns to map inputs to outputs, making predictions on unseen data. Subtopics: Classification: Predicting categorical labels for input data. Regression: Predicting continuous values for input data.

Unsupervised Learning: Unsupervised learning aims to find hidden patterns or structures in unlabeled data. It doesn't rely on labeled outputs but instead explores the data's inherent structure. Subtopics: Clustering: Grouping similar data points together based on certain features. Dimensionality Reduction: Reducing the number of features while preserving the most important information.

Reinforcement Learning: Reinforcement learning involves training an agent to make sequential decisions by interacting with an environment. The agent learns to maximize cumulative rewards through trial and error. Subtopics: Markov Decision Processes (MDPs): Mathematical frameworks for modeling decision-making in dynamic environments. Policy Gradient Methods: Directly optimizing policies to maximize expected rewards.

Deep Learning: Deep learning is a subset of ML that utilizes neural networks with multiple layers (deep architectures) to learn complex patterns from data. Subtopics: Convolutional Neural Networks (CNNs): Particularly effective for image recognition and computer vision tasks. Recurrent Neural Networks (RNNs): Suitable for sequential data such as time series or natural language.

Any alternative approaches/features

Adding proper resources/links for the above stated solution.

Additional Context

Additional Context:

• Transfer Learning: Leveraging knowledge from pre-trained models to improve performance on related tasks with limited labeled data.

• Hyperparameter Tuning: The process of optimizing the hyperparameters of a ML model to maximize its performance on unseen data.

• Ethical Considerations: Addressing biases, fairness, and privacy concerns in ML applications.

• Deployment and Scalability: Challenges and best practices for deploying ML models into production environments and scaling them for real-world usage.

[karishmaaa101]

Assign me this issue.

[jfmartinz]

Please follow the format in how to add a resource or section CONTRIBUTING.md

[karishmaaa101]

Okay sure.