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1. What is Machine Learning?
• Machine Learning (ML) is a field that empowers computers to learn from data without explicit programming.
• It mimics human learning, allowing computers to improve performance over time.
2. Features of Machine Learning:
• Data-driven technology: ML relies on large amounts of data generated by organizations.
• Automatic learning and improvement: Machines can self-learn from past data.
• Pattern detection: ML identifies patterns in datasets.
• Branding and targeting: Useful for organizations to target specific customer bases.
3. Prerequisites to Learn Machine Learning:
• Knowledge of linear equations, graphs, statistics, linear algebra, probability, and calculus.
• Familiarity with programming languages like Python, C++, or R is recommended.
4. Difference Between Machine Learning and Deep Learning:
• Machine Learning: Develops programs that access and learn from data.
• Deep Learning: A subset of machine learning that supports automatic feature extraction from raw data.
5. Types of Machine Learning Algorithms:
• Supervised Algorithms:
• Learn from labeled data.
• Examples: Regression, object detection, segmentation.
• Unsupervised Algorithms:
• Learn from non-labeled data.
• Examples: Clustering, dimensionality reduction.
• Semi-Supervised Algorithms:
• Use both supervised and unsupervised data.
• Example: Anomaly detection.
6. Why Use Machine Learning?
• Decision-making based on data.
• Model algorithms on historical data to find patterns and relationships.
• Predict solutions for unseen problems using learned patterns.
In essence, Machine Learning is a transformative technology that leverages data to enable computers to learn and make informed decisions, offering valuable insights and predictions for various applications.
1. What is Machine Learning?
• Machine Learning (ML) is a field that empowers computers to learn from data without explicit programming.
• It mimics human learning, allowing computers to improve performance over time.
2. Features of Machine Learning:
• Data-driven technology: ML relies on large amounts of data generated by organizations.
• Automatic learning and improvement: Machines can self-learn from past data.
• Pattern detection: ML identifies patterns in datasets.
• Branding and targeting: Useful for organizations to target specific customer bases.
3. Prerequisites to Learn Machine Learning:
• Knowledge of linear equations, graphs, statistics, linear algebra, probability, and calculus.
• Familiarity with programming languages like Python, C++, or R is recommended.
4. Difference Between Machine Learning and Deep Learning:
• Machine Learning: Develops programs that access and learn from data.
• Deep Learning: A subset of machine learning that supports automatic feature extraction from raw data.
5. Types of Machine Learning Algorithms:
• Supervised Algorithms:
• Learn from labeled data.
• Examples: Regression, object detection, segmentation.
• Unsupervised Algorithms:
• Learn from non-labeled data.
• Examples: Clustering, dimensionality reduction.
• Semi-Supervised Algorithms:
• Use both supervised and unsupervised data.
• Example: Anomaly detection.
6. Why Use Machine Learning?
• Decision-making based on data.
• Model algorithms on historical data to find patterns and relationships.
• Predict solutions for unseen problems using learned patterns.
In essence, Machine Learning is a transformative technology that leverages data to enable computers to learn and make informed decisions, offering valuable insights and predictions for various applications.