Overview

Machine Learning with Python Information

Learning Objectives

By the end of this 2-day Machine Learning with Python program, participants will be able to:

1. Understand the fundamentals of machine learning, including supervised, unsupervised, and reinforcement learning.
2. Set up the Python environment and essential libraries (NumPy, Pandas, Scikit-learn, Matplotlib) for machine learning tasks.
3. Import, explore, and preprocess data to prepare it for model building.
4. Apply key machine learning algorithms, including linear regression, logistic regression, decision trees, clustering, and k-nearest neighbors.
5. Train, test, and evaluate machine learning models using appropriate metrics (accuracy, precision, recall, F1-score, RMSE, etc.).
6. Perform feature selection and feature engineering to improve model performance.
7. Understand model overfitting, underfitting, and techniques to prevent them such as cross-validation and regularization.
8. Interpret model outputs and predictions to make data-driven decisions.
9. Deploy simple machine learning models in Python for practical applications.
10. Develop a small end-to-end machine learning project using Python, from data collection to model evaluation.