Machine Learning
Basics of Machine Learning
- Difference between AI and Machine learning
- AI encompasses the broader idea of enabling machines to emulate human-like abilities, such as sensing, reasoning, acting, or adapting.
- ML operates within the realm of AI, focusing on using data to autonomously extract knowledge and learn.
- What is Machine Learning ..?
- Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make decisions or predictions without explicit programming.
- Machine Learning WorkFlow
- Types of Machine Learning
Supervised Learning: Learn about supervised learning, where models are trained using labeled data, making predictions based on previously known outcomes. Dive into use cases such as classification and regression, which are common applications of supervised learning.
Unsupervised Learning: Explore unsupervised learning, which involves training models on unlabeled data to discover hidden patterns and groupings. Understand applications like clustering and dimensionality reduction that fall under this category.
Reinforcement Learning: Discover reinforcement learning, where agents learn by interacting with an environment and receiving feedback in the form of rewards. Learn how this type of learning is applied in scenarios like game playing and autonomous systems.
- AI encompasses the broader idea of enabling machines to emulate human-like abilities, such as sensing, reasoning, acting, or adapting.
- ML operates within the realm of AI, focusing on using data to autonomously extract knowledge and learn.
- Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make decisions or predictions without explicit programming.
Supervised Learning: Learn about supervised learning, where models are trained using labeled data, making predictions based on previously known outcomes. Dive into use cases such as classification and regression, which are common applications of supervised learning.
Unsupervised Learning: Explore unsupervised learning, which involves training models on unlabeled data to discover hidden patterns and groupings. Understand applications like clustering and dimensionality reduction that fall under this category.
Reinforcement Learning: Discover reinforcement learning, where agents learn by interacting with an environment and receiving feedback in the form of rewards. Learn how this type of learning is applied in scenarios like game playing and autonomous systems.
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