Understand the foundational concepts of linear regression, its assumptions, and how to implement it using Python for predictive modeling.

Extend your knowledge to multiple independent variables, learning how to build and interpret models for more complex relationships in data.

Gain practical experience using Scikit-learn, Python’s premier machine learning library, to efficiently perform linear regression analysis.

Apply linear regression techniques to a real-world dataset, solidifying your understanding through a hands-on case study.

Master logistic regression for binary classification problems, learning how to model probabilities and make predictions for categorical outcomes.

Begin your journey into unsupervised learning by understanding the basics of cluster analysis and essential prerequisites for its application.

Dive into K-Means, a popular clustering algorithm, learning its mechanics, implementation in Python, and how to interpret clustering results.

Explore additional clustering techniques beyond K-Means, broadening your toolkit for different data structures and analytical needs.