Machine Learning with Python

Learn machine learning with Python

Machine learning powers everything from recommendation engines to fraud detection and predictive analytics. On this three-week course, you’ll learn how to build, evaluate, and improve machine learning models using Python while developing practical skills used across data science and AI.

You’ll begin by exploring the foundations of machine learning. As you progress, you’ll discover how machine learning models are developed, evaluated, and applied to solve real-world business challenges.

Build and evaluate predictive machine learning models

Learn how to create supervised machine learning models for both regression and classification problems using industry-standard techniques.

You’ll build regression models using linear, polynomial, and ridge regression before exploring classification algorithms such as logistic regression and decision trees.

Explore advanced machine learning techniques

Take your machine learning skills further by working with advanced algorithms used in professional data science projects. You’ll explore Random Forest, Support Vector Machines (SVM), and XGBoost, while learning how to improve model performance through hyperparameter tuning using grid and random search.

You’ll also discover how to interpret model predictions using SHAP values, address class imbalance with SMOTE, and build recommendation engines. Finally, you’ll explore unsupervised learning by applying K-Means, hierarchical clustering, and DBSCAN, before using Principal Component Analysis (PCA) to reduce dimensionality and uncover patterns in complex datasets.

By the end, you’ll have developed the machine learning and Python skills that are sought-after in the data science industry.

This course is designed for aspiring data professionals, software developers, analysts, engineers, and business professionals who want to build practical data science skills with Python. No prior data science or machine learning experience is required, though basic computer literacy and comfort with logical problem-solving will be helpful.

It is ideal for career changers moving into data roles, working professionals looking to add analytics capabilities to their current role, and graduates preparing for data analyst, data scientist, or machine learning engineer positions. It is also valuable for managers and domain experts who want to understand how data-driven decisions are made.

This course is designed for aspiring data professionals, software developers, analysts, engineers, and business professionals who want to build practical data science skills with Python. No prior data science or machine learning experience is required, though basic computer literacy and comfort with logical problem-solving will be helpful.

It is ideal for career changers moving into data roles, working professionals looking to add analytics capabilities to their current role, and graduates preparing for data analyst, data scientist, or machine learning engineer positions. It is also valuable for managers and domain experts who want to understand how data-driven decisions are made.

• Explain machine learning categories, the machine learning lifecycle, and how CRISP-DM structures a machine learning project.
• Apply regression and classification algorithms using Python and scikit-learn to solve predictive modelling problems.
• Interpret model performance using appropriate regression and classification metrics, cross-validation, and baseline comparisons.
• Evaluate regularisation, ensemble methods, hyperparameter configurations, class imbalance treatments, and model explanations to support reliable model selection.
• Compare K-Means, hierarchical clustering, and DBSCAN based on their approaches to identifying structure in unlabelled data.
• Demonstrate the use of PCA to reduce high-dimensional data and visualise patterns and clusters in a reduced feature space.