Section: Machine learning fundamentals | AI Programming with Python | NLearning

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Machine learning fundamentals
This section introduces the core concepts and techniques of machine learning. You will learn about supervised and unsupervised learning, key algorithms (such as linear regression, decision trees, and k-means clustering), and the process of training, evaluating, and tuning models.

This section introduces the core concepts and techniques of machine learning. You will learn about supervised and unsupervised learning, key algorithms (such as linear regression, decision trees, and k-means clustering), and the process of training, evaluating, and tuning models. The focus is on understanding how machine learning models learn from data, make predictions, and improve over time, with hands-on experience using Python libraries like Scikit-learn.
- Select activity Introduction to machine learning algorithms
  
  Introduction to machine learning algorithms Page
- Select activity Supervised learning: regression and classification
  
  Supervised learning: regression and classification Page
- Select activity Unsupervised learning: clustering and dimensionality reduction
  
  Unsupervised learning: clustering and dimensionality reduction Page
- Select activity Model evaluation and performance metrics
  
  Model evaluation and performance metrics Page

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