Course Outline
Module 1: Essential Python for Machine Learning Workflows
• Programme introduction and workspace preparation
Align on learning objectives and establish a reproducible Python ML environment
• Core Python language features (accelerated review)
Refresh syntax, control structures, functions, and patterns prevalent in ML codebases
• Python data structures for ML
Utilising lists, dictionaries, sets, and tuples for features, labels, and metadata
• Comprehensions and functional programming tools
Implementing transformations via comprehensions and higher-order functions
• Object-oriented Python for ML developers
Classes, methods, composition, and practical design choices
• dataclasses and lightweight modelling
Using typed containers for configuration, examples, and results
• Decorators and context managers
Implementing timing, caching, logging, and resource-safe execution patterns
• File handling and path management
Ensuring robust dataset handling and serialization formats
• Exceptions and defensive programming
Writing ML scripts that fail safely and transparently
• Modules, packages, and project structure
Organising reusable ML codebases effectively
• Typing and code quality
Applying type hints, documentation, and lint-friendly structures
Module 2: NumPy, SciPy, and Data Handling Essentials
• NumPy foundations for vectorised computing
Efficient array operations and performance-conscious coding
• Indexing, slicing, broadcasting, and shapes
Safe tensor manipulation and shape reasoning
• Linear algebra basics with NumPy and SciPy
Stable matrix operations and decompositions relevant to ML
• Deep dive into SciPy
Statistics, optimisation, curve fitting, and sparse matrices
• Pandas for tabular ML data
Cleaning, joining, aggregating, and preparing datasets
• Deep dive into scikit-learn
Estimator interface, pipelines, and reproducible workflows
• Data visualisation essentials
Creating diagnostic plots for data exploration and model behaviour analysis
Module 3: Design Patterns for Machine Learning Applications
• Transitioning from notebooks to maintainable projects
Refactoring exploratory code into structured packages
• Configuration management
Externalising parameters and implementing startup validation
• Logging, warnings, and observability
Structured logging for debuggable ML systems
• Building reusable components via OOP and composition
Designing extensible transformers and predictors
• Practical design patterns
Implementing Pipeline, Factory, Registry, Strategy, and Adapter patterns
• Data validation and schema checks
Preventing silent data issues
• Performance profiling and optimisation
Identifying bottlenecks and applying optimisation techniques
• Model I/O and inference interfaces
Ensuring safe persistence and clean prediction interfaces
• End-to-end mini-build
Constructing a production-style ML pipeline with configuration and logging
Module 4: Statistical Learning for Tabular, Text, and Image Data
• Evaluation fundamentals
Train/validation splits, rigorous cross-validation, and business-aligned metrics
• Advanced tabular machine learning
Regularised GLMs, tree ensembles, and leakage-free preprocessing
• Calibration and uncertainty estimation
Platt scaling, isotonic regression, bootstrap methods, and conformal prediction
• Classical NLP techniques
Tokenisation trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modelling
LDA fundamentals and practical limitations
• Classical computer vision methods
HOG, PCA, and feature-based pipelines
• Error analysis
Detecting bias, label noise, and spurious correlations
• Hands-on labs
Leakage-proof tabular pipeline
Text baseline comparison and interpretation
Classical vision baseline with structured failure analysis
Module 5: Neural Networks for Tabular, Text, and Image Data
• Mastering the training loop
Implementing clean PyTorch loops with AMP, clipping, and reproducibility measures
• Optimisation and regularisation techniques
Initialisation, normalisation, optimisers, and schedulers
• Mixed precision and scaling strategies
Gradient accumulation and checkpointing approaches
• Neural networks for tabular data
Categorical embeddings, feature crosses, and ablation studies
• Neural networks for text data
Embeddings, CNNs, BiLSTMs, GRUs, and sequence handling
• Neural networks for vision data
CNN fundamentals and ResNet-style architectures
• Hands-on labs
Developing a reusable training framework
Comparing Tabular NN vs boosting
CNN experiments with augmentation and scheduling
Module 6: Advanced Neural Architectures
• Transfer learning strategies
Freeze and unfreeze patterns, discriminative learning rates
• Transformer architectures for text
Self-attention internals and fine-tuning approaches
• Vision backbones and dense prediction
ResNet, EfficientNet, Vision Transformers, and U-Net concepts
• Advanced tabular architectures
TabTransformer, FT-Transformer, and Deep and Cross networks
• Time series considerations
Temporal splits and covariate shift detection
• PEFT and efficiency techniques
LoRA, distillation, and quantisation trade-offs
• Hands-on labs
Fine-tuning a pretrained text transformer
Fine-tuning a pretrained vision model
Comparing Tabular transformer vs GBDT
Module 7: Generative AI Systems
• Fundamentals of prompting
Structured prompting and controlled generation techniques
• Foundations of LLMs
Tokenisation, instruction tuning, and mitigating hallucinations
• Retrieval-Augmented Generation (RAG)
Chunking, embeddings, hybrid search, and evaluation metrics
• Fine-tuning strategies
LoRA and QLoRA with data quality controls
• Diffusion models
Understanding latent diffusion and practical adaptation
• Synthetic tabular data generation
CTGAN and privacy considerations
• Hands-on labs
Building a production-style RAG mini-application
Validating structured output with schema enforcement
Optional diffusion experimentation
Module 8: AI Agents and MCP
• Agent loop design
Observe, plan, act, reflect, and persist mechanisms
• Agent architectures
ReAct, plan-and-execute, and multi-agent coordination
• Memory management
Episodic, semantic, and scratchpad approaches
• Tool integration and safety
Tool contracts, sandboxing, and defending against prompt injection
• Evaluation frameworks
Replayable traces, task suites, and regression testing
• MCP and protocol-based interoperability
Designing MCP servers with secure tool exposure
• Hands-on labs
Building an agent from scratch
Exposing tools via an MCP-style server
Creating an evaluation harness with safety constraints
Requirements
Participants must possess a functional understanding of Python programming.
This programme is designed for technical professionals at intermediate to advanced levels.
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
