Prerequisites: Familiarity with Python programming and basic understanding of NumPy and Pandas libraries. Foundational knowledge of machine learning concepts (supervised vs. unsupervised learning, classification, regression) is beneficial.

Level: Intermediate

What You'll Learn

• Scikit-learn Core API

  Understand and utilize the main components of the Scikit-learn library, including Estimators, Predictors, and Transformers.

• Data Preparation

  Apply common data preprocessing techniques like scaling, encoding categorical features, and handling missing values using Scikit-learn.

• Supervised Learning Models

  Implement and evaluate common supervised learning algorithms for regression and classification tasks.

• Model Evaluation

  Select appropriate metrics and use cross-validation techniques to assess model performance reliably.

• Pipeline Construction

  Build and manage machine learning workflows using Scikit-learn Pipelines to combine preprocessing and modeling steps.