Course Outline
Module 1: Core Python for ML Workflows
• Course introduction and workspace configuration
Aligning learning objectives and establishing a reproducible Python ML environment
• Accelerated Python syntax review
Revisiting essential syntax, control flow, functions, and patterns frequently found in ML codebases
• Data structures for ML applications
Utilising lists, dictionaries, sets, and tuples to manage features, labels, and metadata
• Comprehensions and functional programming tools
Implementing data transformations through comprehensions and higher-order functions
• Object-oriented Python for ML developers
Exploring classes, methods, composition, and key design decisions in practice
• dataclasses and lightweight data modelling
Creating typed containers for configurations, data examples, and results
• Decorators and context managers
Implementing timing, caching, logging, and resource-safe execution patterns
• File and path management
Ensuring robust dataset handling and efficient serialization formats
• Exception handling and defensive coding
Writing ML scripts that handle errors safely and transparently
• Modules, packages, and project organisation
Structuring reusable ML codebases effectively
• Type hinting and code quality
Incorporating type hints, documentation, and lint-friendly code structures
Module 2: Numerical Python, SciPy, and Data Processing
• NumPy basics for vectorised computation
Performing efficient array operations with an eye on performance
• Indexing, slicing, broadcasting, and shapes
Mastering safe tensor manipulation and shape reasoning
• Essential linear algebra with NumPy and SciPy
Applying stable matrix operations and decompositions relevant to ML
• In-depth SciPy exploration
Covering statistics, optimisation, curve fitting, and sparse matrices
• Pandas for tabular ML data
Cleaning, joining, aggregating, and preparing datasets for analysis
• Comprehensive scikit-learn guide
Understanding the estimator interface, pipelines, and reproducible workflows
• Visualisation basics
Creating diagnostic plots for data exploration and assessing model behaviour
Module 3: Programming Patterns for ML Applications
• Transitioning from notebooks to maintainable projects
Refactoring exploratory code into well-structured packages
• Configuration management
Implementing externalised parameters and startup validation
• Logging, warnings, and observability
Setting up structured logging for debuggable ML systems
• Reusable components via OOP and composition
Designing extensible transformers and predictors
• Applied design patterns
Utilising Pipeline, Factory or Registry, Strategy, and Adapter patterns
• Data validation and schema checks
Mitigating silent data issues through rigorous validation
• Performance optimisation and profiling
Identifying bottlenecks and applying targeted optimisation techniques
• Model I/O and inference interfaces
Ensuring safe persistence and clean prediction interfaces
• End-to-end mini project
Building a production-style ML pipeline with integrated configuration and logging
Module 4: Statistical Learning for Tabular, Text, and Image Data
• Evaluation fundamentals
Implementing train/validation splits, honest cross-validation, and business-aligned metrics
• Advanced tabular ML techniques
Applying regularised GLMs, tree ensembles, and leakage-free preprocessing
• Calibration and uncertainty quantification
Using Platt scaling, isotonic regression, bootstrap, and conformal prediction
• Classical NLP methods
Navigating tokenisation trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modelling
Understanding LDA fundamentals and their practical limitations
• Classical computer vision
Employing HOG, PCA, and feature-based pipelines
• Error analysis
Detecting bias, label noise, and spurious correlations
• Practical laboratories
Building a leakage-proof tabular pipeline
Comparing and interpreting text baselines
Analysing classical vision baselines with structured failure analysis
Module 5: Neural Networks for Tabular, Text, and Image Data
• Mastery of training loops
Implementing clean PyTorch loops with AMP, clipping, and reproducibility
• Optimisation and regularisation strategies
Managing initialisation, normalisation, optimisers, and schedulers
• Mixed precision and scaling
Utilising gradient accumulation and checkpointing strategies
• Tabular neural networks
Integrating categorical embeddings, feature crosses, and ablation studies
• Text neural networks
Using embeddings, CNNs, BiLSTM or GRU, and sequence handling
• Vision neural networks
Exploring CNN fundamentals and ResNet-style architectures
• Practical laboratories
Developing a reusable training framework
Comparing tabular NNs against boosting
Experimenting with CNNs, augmentation, and scheduling
Module 6: Advanced Neural Architectures
• Transfer learning strategies
Applying freeze/unfreeze patterns and discriminative learning rates
• Transformer architectures for text
Understanding self-attention internals and fine-tuning approaches
• Vision backbones and dense prediction
Exploring ResNet, EfficientNet, Vision Transformers, and U-Net concepts
• Advanced tabular architectures
Implementing TabTransformer, FT-Transformer, and Deep and Cross networks
• Time series considerations
Handling temporal splits and detecting covariate shift
• PEFT and efficiency techniques
Assessing LoRA, distillation, and quantisation trade-offs
• Practical laboratories
Fine-tuning a pretrained text transformer
Fine-tuning a pretrained vision model
Comparing tabular transformers against GBDT
Module 7: Generative AI Systems
• Prompting fundamentals
Applying structured prompting and controlled generation
• LLM foundations
Understanding tokenisation, instruction tuning, and hallucination mitigation
• Retrieval-Augmented Generation (RAG)
Implementing chunking, embeddings, hybrid search, and evaluation metrics
• Fine-tuning strategies
Using LoRA and QLoRA with strict data quality controls
• Diffusion models
Understanding latent diffusion intuition and practical adaptation
• Synthetic tabular data
Using CTGAN and addressing privacy considerations
• Practical laboratories
Developing a production-style RAG mini-application
Validating structured outputs with schema enforcement
Optional diffusion experimentation
Module 8: AI Agents and MCP
• Agent loop design
Implementing observe, plan, act, reflect, and persist cycles
• Agent architectures
Exploring ReAct, plan-and-execute, and multi-agent coordination
• Memory management
Utilising episodic, semantic, and scratchpad approaches
• Tool integration and safety
Defining tool contracts, sandboxing, and defending against prompt injection
• Evaluation frameworks
Implementing replayable traces, task suites, and regression testing
• MCP and protocol-based interoperability
Designing MCP servers with secure tool exposure
• Practical laboratories
Building an agent from scratch
Exposing tools via an MCP-style server
Creating an evaluation harness with safety constraints
Requirements
Candidates are expected to possess a functional proficiency in Python programming.
This course is tailored for technical professionals with intermediate to advanced expertise.
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
