Introduction to Machine Learning:
- Overview of machine learning and its applications.
- Introduction to Scikit-Learn as a popular machine learning library in Python.
Loading and Preprocessing Data:
- Importing datasets using Scikit-Learn and other data manipulation libraries.
- Preprocessing steps such as handling missing values, scaling features, and encoding categorical variables.
Building and Evaluating Models:
- Introduction to machine learning algorithms available in Scikit-Learn (e.g., linear regression, decision trees, k-nearest neighbors).
- Splitting data into training and testing sets and training machine learning models.
- Evaluating model performance using metrics such as accuracy, precision, recall, and F1-score.
Practical Example:
- Walkthrough of a simple machine learning project using Scikit-Learn.
- Loading a dataset, preprocessing data, training a model, and evaluating its performance.
Conclusion:
- Summary of key concepts covered.
- Encouragement for readers to explore more advanced machine learning techniques and datasets.