1. What is Machine Learning?
Answer:
Machine Learning is a branch of artificial intelligence (AI) that enables computers to learn from data and make decisions or predictions without being explicitly programmed. It involves algorithms that improve automatically through experience.
2. What are the main types of Machine Learning?
Answer:
Supervised Learning: The model learns from labeled data (e.g., regression, classification).
Unsupervised Learning: The model finds patterns in unlabeled data (e.g., clustering, dimensionality reduction).
Reinforcement Learning: The model learns by interacting with an environment and receiving rewards or penalties.
3. What is the difference between AI, Machine Learning, and Deep Learning?
Answer:
● AI: Broad concept of machines performing tasks that require intelligence.
● Machine Learning: Subset of AI focusing on learning from data.
● Deep Learning: Subset of ML using neural networks with multiple layers.
4. What is overfitting in Machine Learning?
Answer:
Overfitting occurs when a model learns the training data too well, including its noise and outliers, resulting in poor performance on unseen data. It means the model has low bias but high variance.
5. What are common algorithms used in Machine Learning?
Answer:
● Linear Regression
● Logistic Regression
● Decision Trees
● Random Forest
● Support Vector Machines (SVM)
● K-Nearest Neighbors (KNN)
● Naive Bayes
● Neural Networks
● Gradient Boosting (e.g., XGBoost, LightGBM)
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