Machine Learning (ML)
Machine Learning is a subset of AI where algorithms learn patterns from data without being explicitly programmed. Instead of writing rules, you feed the system examples and it discovers the rules on i…
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Definition
Machine Learning is a subset of AI where algorithms learn patterns from data without being explicitly programmed. Instead of writing rules, you feed the system examples and it discovers the rules on its own. ML is the technology behind recommendation engines, fraud detection, and predictive analytics used by Indian companies from Flipkart to HDFC Bank.
Key Points
- Three main types: supervised, unsupervised, and reinforcement learning
- Requires training data — the quality and quantity of data directly affects model accuracy
- Popular frameworks: TensorFlow, PyTorch, scikit-learn
- Indian companies use ML for credit scoring, demand forecasting, and customer segmentation
Frequently Asked Questions
AI is the broad goal of making intelligent machines. ML is a specific approach to achieving AI — by letting computers learn from data. All ML is AI, but not all AI is ML. Rule-based systems and expert systems are AI without ML.
Start with Python programming, then learn statistics and linear algebra fundamentals. Next, study ML algorithms (regression, classification, clustering) and practice with libraries like scikit-learn. Finally, learn deep learning with TensorFlow or PyTorch.
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