Knowledge Graph — Coursera Notes › Academic disciplines › Computer Science / Information Technology

Machine Learning

concept · part of Computer Science / Information Technology

Machine Learning is a subfield of Computer Science that enables systems to learn from data and improve performance without explicit programming. It matters because it powers applications like recommendation systems, fraud detection, and predictive modeling through concepts like loss functions and model evaluation.

Inside Machine Learning (12)

• AI ML Engineering Workflow — The AI ML engineering workflow is a systematic process that most AI/ML projects follow, starting with data collection and moving through data pre-processing, model development, training, evaluation, and deployment.
• Loss Functions — Loss functions measure the difference between predicted and actual values in machine learning models.
• Recommendation systems — Recommendation systems are machine learning algorithms that suggest items to users based on collaborative filtering or content-based filtering.
• Fraud detection — Fraud detection involves identifying fraudulent activities using machine learning, often in real-time.
• Model Architectures — Model architectures refer to the structural design of machine learning models, such as Recurrent Neural Networks (RNNs) and LSTMs, which are specialized for sequential data.
• Predictive modeling — Predictive modeling uses statistical and machine learning techniques to forecast future outcomes, such as demand forecasting with ML.
• Loss function — A loss function measures the difference between a model's output and the desired output.
• MLOps — MLOps is a set of best practices that combine machine learning development with operational practices.
• Model evaluation — TensorFlow's evaluate method simplifies evaluation.
• Overfitting and Underfitting — Training and validation accuracy/loss plots help diagnose overfitting (model performs well on training but poorly on validation) or underfitting (poor performance on both).
• Probabilistic Machine Learning — Probabilistic machine learning uses probability theory to model uncertainty in data and predictions.
• Statistical Learning Theory — Statistical learning theory provides the theoretical foundation for machine learning, explaining how algorithms learn from data.

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