AI & Machine Learning Course
Master Artificial Intelligence & Machine Learning with Hands-On Training. Learn how to build intelligent applications, analyse data, develop predictive models, and solve real-world problems using AI and Machine Learning.
Build your knowledge from foundational concepts to advanced techniques through practical coding, assignments, real-world projects, and industry-focused training.
Build Your Foundation in AI & Machine Learning
Artificial Intelligence and Machine Learning are transforming industries by helping organisations automate processes, make data-informed decisions, improve customer experiences and develop intelligent products.
This course explores the journey from programming and data preparation to machine learning algorithms, deep learning, Generative AI and deployment fundamentals.
Whether you’re beginning your technology journey or expanding your existing skill set, the learning pathway can be discussed with our team based on your experience and goals.
Discuss Your Learning Goals →Understand
Build a foundation in AI concepts, Python, statistics and data preparation.
Develop
Explore algorithms, predictive models, evaluation and optimisation.
Explore
Discover deep learning, NLP, computer vision and Generative AI.
Apply
Work through project ideas and application deployment concepts.
Skills for a Data-Driven World
AI and Machine Learning techniques are used across sectors to discover patterns, support decisions, automate workflows and create new digital experiences.
Technology & Software
Explore intelligent applications, automation and software development use cases.
Finance & Banking
Understand applications such as fraud detection, forecasting and risk analysis.
Healthcare
Discover how data models can support research and selected analytical workflows.
E-commerce & Marketing
Explore recommendations, customer segmentation and sentiment analysis.
Manufacturing & Automotive
Learn about prediction, computer vision and process optimisation use cases.
Cybersecurity & BI
Explore anomaly detection, data analysis and decision-support applications.
What You Will Learn
Explore the 16 learning modules below. Expand any module to see the topics covered in this AI & Machine Learning curriculum.
01 Introduction to Artificial Intelligence
Understand the foundations of AI, its evolution and how intelligent systems are used in real-world scenarios.
- What is Artificial Intelligence?
- History and evolution of AI
- AI applications and use cases
- AI vs Machine Learning vs Deep Learning
- Types of Artificial Intelligence
- AI development lifecycle
- Real-world AI applications
- AI career opportunities
02 Python Programming for AI & ML
Build a Python foundation for data analysis, automation and machine learning development.
- Python fundamentals
- Variables and data types
- Operators and expressions
- Conditional statements
- Loops and functions
- Lists, tuples, sets and dictionaries
- Object-oriented programming basics
- File handling and exception handling
- Working with libraries
- NumPy
- Pandas
03 Mathematics & Statistics for Machine Learning
Explore mathematical and statistical concepts that help explain how machine learning models work.
- Descriptive statistics
- Probability fundamentals
- Probability distributions
- Mean, median and mode
- Variance and standard deviation
- Correlation and covariance
- Linear algebra fundamentals
- Vectors and matrices
- Basic calculus concepts
- Statistical inference
- Hypothesis testing
04 Data Preprocessing & Exploratory Data Analysis
Learn to prepare datasets, identify data-quality issues and explore patterns before modelling.
- Data collection
- Data cleaning
- Handling missing values
- Duplicate data
- Outlier detection
- Data transformation
- Feature engineering
- Feature selection
- Categorical data encoding
- Normalisation and standardisation
- Exploratory Data Analysis
- Data visualisation
05 Supervised Machine Learning
Understand algorithms that learn from labelled datasets to make predictions or classify information.
- Introduction to supervised learning
- Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbors
- Support Vector Machines
- Naive Bayes
- Regression problems
- Classification problems
06 Unsupervised Machine Learning
Discover how models identify patterns, clusters and structure in datasets without labelled outcomes.
- Introduction to unsupervised learning
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Dimensionality reduction
- Principal Component Analysis
- Customer segmentation
- Pattern recognition
07 Machine Learning Model Evaluation
Learn how to evaluate model performance and recognise common modelling problems.
- Training and testing datasets
- Validation techniques
- Cross-validation
- Confusion matrix
- Accuracy, precision and recall
- F1-score
- ROC-AUC
- Regression evaluation metrics
- Overfitting and underfitting
- Bias and variance
- Model selection
08 Feature Engineering & Model Optimisation
Improve model inputs and explore techniques for tuning machine learning performance.
- Feature creation
- Feature selection
- Feature transformation
- Handling imbalanced datasets
- Dimensionality reduction
- Hyperparameter tuning
- Grid Search
- Random Search
- Model optimisation
09 Advanced Machine Learning
Explore ensemble methods and approaches for comparing and improving predictive models.
- Ensemble learning
- Bagging
- Boosting
- Gradient Boosting
- Random Forest
- XGBoost fundamentals
- Model stacking
- Model comparison
- Feature importance
10 Deep Learning Fundamentals
Understand neural networks and the basic concepts behind deep learning systems.
- Introduction to Deep Learning
- Neural networks
- Artificial neurons
- Activation functions
- Forward propagation
- Backpropagation
- Loss functions
- Optimisers
- Training neural networks
- Deep neural networks
- Introduction to TensorFlow and PyTorch
11 Natural Language Processing
Explore techniques that enable computer systems to process and analyse human language.
- Introduction to NLP
- Text preprocessing
- Tokenisation
- Stop-word removal
- Stemming and lemmatisation
- Text vectorisation
- Bag of Words
- TF-IDF
- Text classification
- Sentiment analysis
- Introduction to embeddings
12 Computer Vision Fundamentals
Discover how AI systems process visual information and identify patterns in images.
- Introduction to Computer Vision
- Image processing fundamentals
- Image classification
- Feature extraction
- Convolutional Neural Networks
- Image recognition
- Object detection concepts
- Real-world computer vision applications
13 Introduction to Generative AI
Understand how modern generative systems produce text, images, code and other types of content.
- What is Generative AI?
- Traditional AI vs Generative AI
- Foundation models
- Large Language Models
- Text generation
- Image generation
- Code generation
- Generative AI applications
- AI-powered business solutions
14 Large Language Models & Prompt Engineering
Explore the fundamentals of language models and ways to structure prompts for AI tasks.
- Introduction to LLMs
- How LLMs work
- Transformers
- Tokens and tokenisation
- Embeddings
- Context windows
- Prompt engineering
- Zero-shot prompting
- Few-shot prompting
- Role-based prompting
- Structured prompts
- Working with AI APIs
15 AI Application Development
Explore how models and AI services can be integrated into practical applications.
- Building ML-powered applications
- Working with APIs
- Model integration
- AI application architecture
- Creating prediction services
- Building AI assistants
- Application testing
- Deployment fundamentals
16 Model Deployment & MLOps Fundamentals
Learn the concepts involved in preparing, deploying and monitoring models beyond experimentation.
- Model serialisation
- API development
- Deployment fundamentals
- Model serving
- Version control with Git
- Model monitoring
- Performance monitoring
- Introduction to MLOps
- Production considerations
Hands-On Projects
Explore project ideas that connect AI and Machine Learning concepts with practical business and technical problems.
Machine Learning Projects
- House Price Prediction — develop a regression model to estimate property prices.
- Customer Churn Prediction — identify customers who may leave a business.
- Customer Segmentation — group customers according to their behaviour.
- Fraud Detection — explore identifying suspicious transactions.
- Sales Prediction — forecast future sales using historical data.
AI Application Projects
- Sentiment Analysis System — classify the sentiment of customer reviews.
- AI Recommendation System — recommend products or content using user behaviour.
- Image Classification — build a model to classify images.
- AI Chatbot — develop an intelligent conversational assistant.
- Document Intelligence — extract and analyse information from documents.
Project availability and depth may depend on the final training plan agreed with the training provider.
Tools & Technologies
Explore commonly used tools and technologies across data analysis, Machine Learning, deep learning and AI application development.
Is This Course Right for You?
This learning pathway may suit people from different educational and professional backgrounds who want to explore AI and Machine Learning.
Basic programming knowledge can be helpful. If you’re new to Python or AI, tell us about your background so our team can discuss a suitable starting point with you.
Explore AI & Machine Learning Career Options
The skills covered in this field are relevant to a range of technical roles. Actual requirements vary by employer, experience and location.
AI Engineer
Work on integrating AI techniques and models into software solutions.
Machine Learning Engineer
Develop, evaluate and maintain machine learning systems.
Junior Data Scientist
Use statistics, data analysis and modelling to investigate problems.
AI/ML Developer
Build software features that use predictive models or AI services.
Data Scientist
Analyse data and develop models to support business decisions.
NLP Engineer
Work on applications that process or analyse human language.
Computer Vision Engineer
Develop systems that interpret images and visual information.
AI Application Developer
Integrate AI models, services and APIs into user-facing applications.
ML Analyst / AI Solutions Developer
Support model analysis or develop solutions around AI use cases.
Why Explore This AI & Machine Learning Course?
Review the curriculum and discuss how its topics, projects and learning support align with your goals before choosing a training option.
- Industry-oriented curriculum
- Beginner-to-advanced learning pathway
- Hands-on coding practice and practical assignments
- Real-world project ideas
- Industry-relevant tools and technologies
- Interview preparation and resume-building support options
- Career guidance and flexible learning options to discuss
- Certificate of completion, subject to the provider’s programme terms
What You Can Work Towards
By working through the curriculum and its practical activities, learners can develop familiarity with these areas:
Start Your AI & Machine Learning Journey
Develop practical AI and Machine Learning skills and explore how this field could fit your next career or professional development goal.
Learn AI. Build ML Models. Solve Real-World Problems.
Interested in AI & Machine Learning Training?
Tell us a little about your background and goals. Our team will contact you to discuss suitable training options and answer your questions about the curriculum.
Prefer to discuss first? Submit an enquiry and request a conversation with our team.
Submitting an enquiry does not enrol you in a course. Training availability, format, fees and programme details should be confirmed with the provider.