CS · ML

Machine Learning

20 hours, 20 minutes 20 Seconds

Description

Machine learning underpins everything from recommendation engines to fraud detection. This course covers supervised and unsupervised learning, model evaluation, and feature engineering, giving students the mathematical intuition and practical skills to build and evaluate ML models on real datasets.

What Students Will Learn

+ Supervised Learning: Regression & Classification ›
+ Unsupervised Learning and Clustering ›
+ Model Evaluation and Validation ›
+ Feature Engineering and Selection ›

Overall Learning Outcomes

  1. Algorithmic Fluency: Implement and apply core supervised and unsupervised learning algorithms.
  2. Evaluation Skills: Assess model performance using appropriate validation techniques.
  3. Feature Engineering: Transform raw data into effective features for model training.
  4. Applied Modeling: Build and tune machine learning models on real-world datasets.
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