This project demonstrates the implementation of various machine learning models using popular Python libraries such as NumPy, scikit-learn, and TensorFlow. The models are built, trained, and evaluated for tasks such as prediction, binary classification, and multi-class classification.
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Supervised Learning Models:
Build and train machine learning models for both prediction and binary classification tasks, including:- Linear Regression
- Logistic Regression (Using NumPy & scikit-learn)
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Neural Networks with TensorFlow:
Develop and train a neural network for performing binary-class classification tasks. Develop and train a neural network for performing multi-class classification tasks. -
Decision Trees and Ensemble Methods:
Implement decision trees and tree ensemble methods, including random forests and boosted trees, for various classification and regression tasks. -
Best Practices:
Practiced multiple labs for machine learning development to ensure models generalize well to real-world data and tasks.