Course
Machine Learning Training
Build and evaluate machine learning models using Python, from supervised learning fundamentals to model optimization and ensemble methods.
Who this is for
Designed for learners with Python and basic statistics knowledge who want to build machine learning models and solve real-world predictive problems. Suitable for data analysts, software developers, aspiring data scientists, engineers, and professionals who want to move beyond data analysis into intelligent decision-making systems.
Machine Learning enables computers to identify patterns, make predictions, and support data-driven decision making. This course provides a practical introduction to modern machine learning using Python and Scikit-learn, combining theoretical concepts with extensive hands-on implementation. You will learn the complete machine learning workflow, including data preprocessing, feature engineering, supervised and unsupervised learning, model evaluation, feature selection, dimensionality reduction, hyperparameter tuning, ensemble learning, and model interpretation. Throughout the course, you will work with real-world datasets, compare algorithms, optimize model performance, and develop practical machine learning solutions. The course concludes with an end-to-end predictive modeling project that integrates the techniques covered during the program.
What you'll learn
Machine Learning foundations, supervised and unsupervised learning.
Data preprocessing, feature engineering, and feature selection.
Regression, classification, clustering, and dimensionality reduction.
Model evaluation, cross-validation, performance metrics, and error analysis.
Hyperparameter tuning, pipeline optimization, and model selection.
Ensemble learning
Bagging, Random Forest, Boosting, XGBoost, and Stacking.
Model interpretation and explainable AI fundamentals.
10 weeks
