AI Vibe Coding for Machine Learning

About This Course

This course equips participants with practical skills to use AI vibe coding and Python to build machine learning solutions using scikit-learn. Learners will use natural-language instructions and AI coding assistants to generate, explain, test, debug, and refine Python code, making machine learning development faster and more accessible.

Participants will learn how to prepare data for machine learning by importing, cleaning, transforming, encoding, and organising datasets. They will use scikit-learn to develop machine learning workflows covering data preprocessing, feature selection, model training, prediction, and evaluation.

The course covers key supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. Learners will explore commonly used algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbours, K-Means Clustering, and Principal Component Analysis (PCA).

Using AI-assisted workflows, learners will generate and refine scikit-learn code, select suitable algorithms, troubleshoot errors, and compare model performance. They will apply train-test splitting, cross-validation, feature scaling, hyperparameter tuning, and scikit-learn Pipelines to improve model reliability.

Through hands-on projects, participants will build end-to-end machine learning solutions, from preparing data and training models to evaluating, improving, and interpreting results. Emphasis is placed on validating AI-generated code, preventing overfitting and data leakage, and selecting appropriate evaluation metrics.

By the end of the course, learners will be able to use AI vibe coding, Python, and scikit-learn to develop and evaluate practical machine learning solutions.