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 prevalent in ML codebases
• Data structures for ML
Utilize lists, dictionaries, sets, and tuples for features, labels, and metadata
• Comprehensions and functional tools
Implement transformations using comprehensions and higher-order functions
• Object-oriented Python for ML developers
Explore classes, methods, composition, and practical design decisions
• dataclasses and lightweight modelling
Create typed containers for configuration, examples, and results
• Decorators and context managers
Apply timing, caching, logging, and resource-safe execution patterns
• Working with files and paths
Handle robust datasets and serialization formats
• Exceptions and defensive programming
Develop ML scripts that fail safely and transparently
• Modules, packages and project structure
Organize reusable ML codebases effectively
• Typing and code quality
Employ type hints, documentation, and lint-friendly structures
Module 2: Numerical Python, SciPy and Data Handling
• NumPy foundations for vectorised computing
Master efficient array operations and performance-aware coding
• Indexing, slicing, broadcasting and shapes
Ensure safe tensor manipulation and shape reasoning
• Linear algebra essentials with NumPy and SciPy
Perform stable matrix operations and decompositions used in ML
• SciPy deep dive
Explore statistics, optimisation, curve fitting, and sparse matrices
• Pandas for tabular ML data
Clean, join, aggregate, and prepare datasets
• scikit-learn deep dive
Understand the estimator interface, pipelines, and reproducible workflows
• Visualisation essentials
Create diagnostic plots for data exploration and model behaviour analysis
Module 3: Programming Patterns for Building ML Applications
• From notebook to maintainable project
Refactor exploratory code into structured packages
• Configuration management
Manage externalised parameters and implement startup validation
• Logging, warnings and observability
Implement structured logging for debuggable ML systems
• Reusable components with OOP and composition
Design extensible transformers and predictors
• Practical design patterns
Apply Pipeline, Factory or Registry, Strategy, and Adapter patterns
• Data validation and schema checks
Prevent silent data issues through rigorous validation
• Performance and profiling
Identify bottlenecks and apply optimisation techniques
• Model I O and inference interfaces
Ensure safe persistence and clean prediction interfaces
• End-to-end mini build
Construct a production-style ML pipeline with configuration and logging
Module 4: Statistical Learning for Tabular, Text and Image
• Evaluation foundations
Manage train and validation splits, ensure honest cross-validation, and align with business metrics
• Advanced tabular ML
Utilize regularised GLMs, tree ensembles, and leakage-free preprocessing
• Calibration and uncertainty
Apply Platt scaling, isotonic regression, bootstrap, and conformal prediction
• Classical NLP methods
Understand tokenisation trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modelling
Explore LDA fundamentals and practical limitations
• Classical computer vision
Implement HOG, PCA, and feature-based pipelines
• Error analysis
Detect 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
• Training loop mastery
Develop clean PyTorch loops with AMP, clipping, and reproducibility
• Optimisation and regularisation
Master initialisation, normalisation, optimisers, and schedulers
• Mixed precision and scaling
Implement gradient accumulation and checkpointing strategies
• Tabular neural networks
Use categorical embeddings, feature crosses, and ablation studies
• Text neural networks
Work with embeddings, CNNs, BiLSTM or GRU, and sequence handling
• Vision neural networks
Understand 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
Employ freeze and unfreeze patterns and discriminative learning rates
• Transformer architectures for text
Explore self-attention internals and fine-tuning approaches
• Vision backbones and dense prediction
Understand ResNet, EfficientNet, Vision Transformers, and U-Net concepts
• Advanced tabular architectures
Explore TabTransformer, FT-Transformer, and Deep and Cross networks
• Time series considerations
Manage temporal splits and detect covariate shift
• PEFT and efficiency techniques
Navigate 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
Implement structured prompting and controlled generation
• LLM foundations
Understand tokenisation, instruction tuning, and hallucination mitigation
• Retrieval-Augmented Generation
Master chunking, embeddings, hybrid search, and evaluation metrics
• Fine-tuning strategies
Apply LoRA and QLoRA with data quality controls
• Diffusion models
Grasp latent diffusion intuition and practical adaptation
• Synthetic tabular data
Utilize CTGAN and address 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
Implement observe, plan, act, reflect, and persist cycles
• Agent architectures
Explore ReAct, plan-and-execute, and multi-agent coordination
• Memory management
Utilize episodic, semantic, and scratchpad approaches
• Tool integration and safety
Establish tool contracts, sandboxing, and prompt injection defences
• Evaluation frameworks
Create replayable traces, task suites, and regression testing protocols
• MCP and protocol-based interoperability
Design 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 working knowledge 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
