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From Data to Decisions: Understanding Machine Learning Algorithms💻
- Machine learning is a subset of artificial intelligence (AI) that focuses on building systems that can learn from data.
- Machine learning algorithms analyze patterns in data to make predictions or decisions.
- Machine learning algorithms follow these steps: Data Collection, Data Preparation, Choosing an Algorithm, Training the Model, Testing the Model, Making Predictions.
- In supervised learning, algorithms are trained using labeled data.
- Unsupervised learning algorithms work with unlabeled data. The goal is to find hidden patterns or groupings within the data.
- Reinforcement learning involves training algorithms through trial and error by interacting with an environment.
- Selecting the right machine learning algorithm depends on several factors.
- Machine learning algorithms are used in various industries for numerous applications: Healthcare, Finance, Marketing and Transportation.
- Machine learning plays a crucial role in transforming raw data into meaningful insights that drive decision-making.
- Experimenting with different algorithms on real datasets will deepen your understanding and help you become proficient in this exciting field!
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