Introduction to Machine Learning with Scikit-Learn

Prep Time:

1-2 hours

Cook Time:

Emphasize ethical considerations in data usage and model deployment.
Guide students on responsible handling of personal or sensitive data in machine learning experiments

Serves:

15+ years

Level:

Intermediate

About the Recipe

Gain practical experience in implementing machine learning algorithms using Scikit-Learn.
Understand the process of data preprocessing, model training, evaluation, and validation.
Develop critical thinking and problem-solving skills through hands-on machine learning projects.

Computer with Python programming environment (e.g., Anaconda with Jupyter Notebook)
Scikit-Learn library installed in Python
Datasets such as Iris dataset (for classification) and Boston housing dataset (for regression)

Introduction to Machine Learning:
- Explain the basic concepts of machine learning, supervised learning, classification, and regression.
- Introduce Scikit-Learn as a popular Python library for machine learning.
Setting Up the Environment:
- Install Scikit-Learn and necessary Python libraries.
- Load and preprocess the dataset (e.g., Iris dataset for classification, Boston housing dataset for regression).
Implementing Classification:
- Guide learners through building a classification model using Scikit-Learn.
- Discuss model selection, training, and evaluation metrics (e.g., accuracy, confusion matrix).
Implementing Regression:
- Teach how to implement a regression model using the Boston housing dataset.
- Cover regression metrics like mean squared error (MSE) and R-squared.
Model Evaluation and Validation:
- Demonstrate techniques for model evaluation and validation using cross-validation and train-test split.
- Discuss overfitting and underfitting, and methods to address them.
Application and Discussion:
- Discuss real-world applications of machine learning in fields such as healthcare, finance, and marketing.
- Encourage students to propose and discuss their own machine learning projects.