Machine Learning Concepts
About the Collection
This collection is a comprehensive exhibit of machine learning concepts, showcasing a range of algorithms and techniques fundamental to the field. Each file within this repository represents a key concept, from data preprocessing to complex predictive modeling.
Concepts Overview
- Decision Trees: Visualizing decision-making processes.
- Dummy Variables & One Hot Encoding: Techniques for categorical data representation.
- Gradient Descent: An optimization algorithm for learning.
- Logistic Regression: A statistical method for binary classification.
- Train Test Split: Methodology for model validation.
Learning Outcomes
Understanding these concepts will provide a solid foundation for anyone interested in applying machine learning algorithms to real-world problems. By exploring these examples, learners can grasp the intricacies of model training, selection, and evaluation.
Tools and Techniques Used
- Python: The primary programming language for demonstrating the concepts.
- Scikit-learn: A machine learning library for Python used in these examples.
- Jupyter Notebooks: Interactive coding environment where the concepts are illustrated.