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Machine Learning
- Machine Learning (ML) is a part of Artificial Intelligence (AI) that involves creating algorithms to learn and adapt from data without being explicitly programmed.
- Unlike traditional programming that follows a set of logical rules and algorithms defined by the developer, ML algorithms operate through a training process where they identify patterns and adjust parameters to minimize the error between predicted and actual results in training data.
- ML has various applications, from pattern recognition in images and texts to financial forecasting and medical diagnoses.
- Machine Learning is everywhere, shaping interactions in our daily life like YouTube recommendations, social media newsfeed, etc.
- Supervised learning is used in problems where labeled data examples are provided and the model learns the relationship between inputs and outputs, while Unsupervised learning is used to identify patterns in the data without the guidance of known outputs.
- Reinforcement learning involves learning from interactions with a dynamic environment and the focus is to learn a policy that maximizes the accumulated reward over time.
- Privacy, data security, and model interpretability are among the main ethical concerns with Machine Learning models that must be addressed responsibly during the process of development and application.
- A thorough process of data collection, preprocessing, model selection, training, evaluation, and validation is a crucial step for ensuring the effectiveness of ML models in solving real-world problems.
- Various libraries and models in Python such as Linear Regression, K-Means Clustering, and Decision Tree are commonly used to demonstrate ML techniques and concepts.
- Identifying and mitigating bias in data and algorithms is crucial to ensure fair and equitable results while promoting trust and acceptance of these technologies.
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