Course Outline
Module 1: Core Python for ML Workflows
• Course kickoff and environment setup
Align objectives and establish a reproducible Python ML workspace.
• Python language essentials (fast-track)
Review syntax, control flow, functions, and patterns common in ML codebases.
• Data structures for ML
Utilise lists, dictionaries, sets, and tuples for features, labels, and metadata.
• Comprehensions and functional tools
Express transformations using comprehensions and higher-order functions.
• Object-oriented Python for ML developers
Classes, methods, composition, and practical design decisions.
• dataclasses and lightweight modelling
Typed containers for configuration, examples, and results.
• Decorators and context managers
Timing, caching, logging, and resource-safe execution patterns.
• Working with files and paths
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.
• Typing and code quality
Type hints, documentation, and lint-friendly structure.
Module 2: Numerical Python, SciPy and Data Handling
• NumPy foundations for vectorised computing
Efficient array operations and performance-aware coding.
• Indexing, slicing, broadcasting and shapes
Safe tensor manipulation and shape reasoning.
• Linear algebra essentials with NumPy and SciPy
Stable matrix operations and decompositions used in ML.
• SciPy deep dive
Statistics, optimisation, curve fitting, and sparse matrices.
• Pandas for tabular ML data
Cleaning, joining, aggregating, and preparing datasets.
• scikit-learn deep dive
Estimator interface, pipelines, and reproducible workflows.
• Visualisation essentials
Diagnostic plots for data exploration and model behaviour.
Module 3: Programming Patterns for Building ML Applications
• From notebook to maintainable project
Refactoring exploratory code into structured packages.
• Configuration management
Externalised parameters and startup validation.
• Logging, warnings and observability
Structured logging for debuggable ML systems.
• Reusable components with OOP and composition
Designing extensible transformers and predictors.
• Practical design patterns
Pipeline, Factory or Registry, Strategy, and Adapter patterns.
• Data validation and schema checks
Preventing silent data issues.
• Performance and profiling
Identifying bottlenecks and applying optimisation techniques.
• Model I O and inference interfaces
Safe persistence and clean prediction interfaces.
• End-to-end mini build
Production-style ML pipeline with configuration and logging.
Module 4: Statistical Learning for Tabular, Text and Image
• Evaluation foundations
Train and validation splits, honest cross-validation, and business-aligned metrics.
• Advanced tabular ML
Regularised GLMs, tree ensembles, and leakage-free preprocessing.
• Calibration and uncertainty
Platt scaling, isotonic regression, bootstrap, and conformal prediction.
• Classical NLP methods
Tokenisation trade-offs, TF-IDF, linear models, and Naive Bayes.
• Topic modelling
LDA fundamentals and practical limitations.
• Classical computer vision
HOG, PCA, and feature-based pipelines.
• Error analysis
Bias detection, 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
• Training loop mastery
Clean PyTorch loops with AMP, clipping, and reproducibility.
• Optimisation and regularisation
Initialisation, normalisation, optimisers, and schedulers.
• Mixed precision and scaling
Gradient accumulation and checkpointing strategies.
• Tabular neural networks
Categorical embeddings, feature crosses, and ablation studies.
• Text neural networks
Embeddings, CNNs, BiLSTM or GRU, and sequence handling.
• Vision neural networks
CNN fundamentals and ResNet-style architectures.
• Hands-on labs
Reusable training framework.
Tabular NN vs boosting comparison.
CNN with augmentation and scheduling experiments.
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 pretrained text transformer.
Fine-tuning pretrained vision model.
Tabular transformer vs GBDT comparison.
Module 7: Generative AI Systems
• Prompting fundamentals
Structured prompting and controlled generation.
• LLM foundations
Tokenisation, instruction tuning, and hallucination mitigation.
• Retrieval-Augmented Generation
Chunking, embeddings, hybrid search, and evaluation metrics.
• Fine-tuning strategies
LoRA and QLoRA with data quality controls.
• Diffusion models
Latent diffusion intuition and practical adaptation.
• Synthetic tabular data
CTGAN and privacy considerations.
• Hands-on labs
Production-style RAG mini-application.
Structured output validation with schema enforcement.
Optional diffusion experimentation.
Module 8: AI Agents and MCP
• Agent loop design
Observe, plan, act, reflect, and persist.
• 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 prompt injection defences.
• Evaluation frameworks
Replayable traces, task suites, and regression testing.
• MCP and protocol-based interoperability
Designing MCP servers with secure tool exposure.
• Hands-on labs
Build an agent from scratch.
Expose tools via MCP-style server.
Create evaluation harness with safety constraints.
Requirements
Participants should possess a functional understanding of Python programming.
This programme is designed for intermediate to advanced technical professionals.
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
