Machine Learning

What you'll learn

• Explain core ML concepts, problem types, and common algorithms
• Prepare and preprocess real-world datasets for modeling
• Perform exploratory data analysis to uncover patterns and issues
• Implement supervised algorithms (regression, classification, trees, SVM)
• Apply unsupervised methods (clustering, dimensionality reduction)
• Design, train, and evaluate neural networks and basic deep learning models
• Engineer features and use ensemble methods to boost performance
• Build simple NLP and computer vision pipelines and deploy models

Prerequisites

Basic Python and comfort with algebra/probability; prior statistics or linear algebra helpful.

Skills you'll gain

• Machine learning fundamentals
• Data preprocessing
• Exploratory data analysis
• Feature engineering
• Model evaluation and validation
• Supervised and unsupervised learning
• Deep learning basics
• NLP pipelines
• Computer vision fundamentals
• Model deployment concepts