Machine learning is no longer confined to research labs. It is at the heart of applications that power recommendation engines, fraud detection systems, medical diagnostics and countless other business-critical processes. Professionals who can design, train and deploy machine learning models are in high demand.
This three day course offers a complete introduction to machine learning, balancing the essential theory with practical implementation. You will work through the full machine learning lifecycle, from preparing raw data to deploying and monitoring models in production environments. Along the way, you will gain hands-on experience with supervised and unsupervised learning techniques, as well as an introduction to deep learning.
Unlike short tutorials, this course is designed to ensure you can apply machine learning techniques in real projects. Case studies and lab sessions are included to help you put theory into practice and see how machine learning creates value in real organisations.
This course is intended for software developers, data analysts and IT professionals who want to build their skills in machine learning. It is suitable for anyone with experience in programming and data handling who is looking to understand both the mathematics and the practical applications of machine learning.
You should have:
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Basic Python programming experience
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Familiarity with common data structures and algorithms
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A working knowledge of basic statistics (mean, median, variance, standard deviation)
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Experience handling tabular data
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Comfort with using the command line for environment setup
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9 lesson covers:
Machine Learning Landscape
This opening module sets the context by contrasting machine learning with traditional programming approaches. You will examine supervised, unsupervised and reinforcement learning, explore the machine learning lifecycle and review popular frameworks within the Python ecosystem.
Mathematical Foundations
Here you will refresh the key mathematical concepts required for machine learning, including linear algebra, calculus, probability and statistics. You will also see how weak mathematical intuition can lead to poor algorithm choices and failed models.
Data Preprocessing Techniques
This module explores how to prepare raw data for modelling. You will learn strategies for dealing with missing values and outliers, feature engineering, encoding categorical variables, class imbalance handling and scaling methods that improve algorithm performance.
Regression Analysis
You will study linear regression, regularisation methods such as L1 and L2, and tree-based approaches for capturing non-linear relationships. Practical labs demonstrate how to evaluate regression pipelines and interpret results in a business context.
Classification Systems
This section introduces logistic regression, decision trees, ensemble methods, support vector machines and techniques for managing imbalanced data. Case studies illustrate the trade-offs between precision and recall in high-stakes systems.
Model Evaluation and Optimisation
Here you will learn how to validate and improve models using cross-validation, hyperparameter tuning, learning curves and feature selection methods. You will also practise systematic optimisation of an ML pipeline to achieve measurable improvements.
Unsupervised Learning Applications
This module covers clustering methods such as K-means and DBSCAN, as well as dimensionality reduction using PCA and t-SNE. You will also explore anomaly detection and association rule mining, with labs focused on customer segmentation.
Deep Learning Fundamentals
You will gain an introduction to neural networks, including architectures, activation functions and optimisation techniques. The module also explores transfer learning and practical issues that arise when implementing deep learning solutions.
Deployment Lifecycle Management
The final module looks at how to move models into production. You will learn about packaging, containerisation, continuous integration and deployment pipelines, as well as methods for monitoring performance and addressing model drift.
