Applied Machine Learning with Python

Course overview

A practical, project-based machine learning programme designed to take learners from data preparation and problem definition to model development, evaluation and deployment. Students work with real-world datasets, learn both supervised and unsupervised learning techniques, develop production-ready workflows and complete portfolio projects that demonstrate practical machine learning skills.

Who it's for: Data analysts, software developers, data professionals, graduates and technology practitioners with basic Python knowledge who want to develop practical machine learning skills.

What you will learn

• Understand the fundamentals of machine learning and its real-world applications
• Translate business problems into appropriate machine learning tasks
• Prepare, clean and explore datasets for machine learning
• Handle missing values, outliers, categorical variables and imbalanced datasets
• Perform feature engineering and feature selection
• Build regression models for prediction and forecasting
• Build classification models for business decision-making
• Train and evaluate tree-based and ensemble models
• Apply clustering and dimensionality reduction techniques
• Use cross-validation and appropriate evaluation metrics
• Tune model hyperparameters and compare competing models
• Build reproducible machine learning pipelines
• Deploy a trained model through an API
• Monitor model performance and understand model drift
• Build portfolio-ready machine learning projects

Curriculum

Tools you'll use

Projects you'll build

• House Price Prediction Model
• Customer Churn Classification System
• Customer Segmentation with Clustering
• Fraud or Anomaly Detection Model
• End-to-End Machine Learning Deployment Project

Prerequisites

• • Basic Python programming
• • Basic understanding of data analysis
• • Comfort working with spreadsheets or SQL
• • Basic statistics is recommended

Frequently asked questions