Location

Community Services & Continuing Education - Alexandria

Objectives

• • Develop a foundational understanding of machine learning concepts, focusing on practical applications using Scikit-Learn.• Learn to prepare data for machine learning by performing necessary transformations, handling missing values, and scaling.• Understand and implement various machine learning algorithms, including regression, classification, and clustering techniques.• Gain skills in model evaluation, tuning, and cross-validation to improve model performance and robustness.

Outcomes

• Understanding Machine Learning Basics: Recognize key machine learning concepts, including types of learning and practical applications.• Proficiency in Data Preparation: Perform data cleaning, transformation, and scaling to prepare datasets for machine learning.• Model Implementation Skills: Implement and interpret linear and logistic regression, decision trees, ensemble methods, KNN, and SVM.• Clustering and Dimensionality Reduction Skills: Apply clustering techniques and reduce data dimensionality for better data visualization and modeling.• Evaluation and Tuning Techniques: Evaluate model performance using appropriate metrics and improve model accuracy with tuning and cross-validation

Course Contents

• Introduction to Machine Learning Concepts and Scikit-Learn: Introduces the basics of machine learning, including supervised and unsupervised learning, and provides an overview of Scikit-Learn for implementing machine learning models in Python.• Data Preparation for Machine Learning: Covers data cleaning, handling missing values, feature scaling, encoding categorical variables, and splitting data into training and testing sets.• Linear Regression and Evaluation Metrics: Introduces linear regression for predictive modeling, along with evaluation metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared to assess model accuracy.• Logistic Regression for Classification: Covers logistic regression for binary classification problems, focusing on model interpretation and evaluation with metrics like accuracy, precision, recall, and F1-score.• Decision Trees and Ensemble Methods: Introduces decision tree models for classification and regression, along with ensemble