Course Outline
Module 1: Essential Python for Machine Learning Workflows
• Programme introduction and environment setup
Aligning objectives and establishing a reproducible Python ML workspace
• Python language essentials (accelerated)
Reviewing syntax, control flow, functions, and patterns prevalent in ML codebases
• Data structures for ML
Utilizing lists, dictionaries, sets, and tuples for features, labels, and metadata
• Comprehensions and functional tools
Implementing transformations via comprehensions and higher-order functions
• Object-oriented Python for ML developers
Working with classes, methods, composition, and practical design decisions
• dataclasses and lightweight modelling
Using typed containers for configuration, examples, and results
• Decorators and context managers
Applying timing, caching, logging, and resource-safe execution patterns
• Working with files and paths
Managing robust datasets and serialization formats
• Exceptions and defensive programming
Writing ML scripts that fail safely and transparently
• Modules, packages, and project structure
Organizing reusable ML codebases effectively
• Typing and code quality
Incorporating type hints, documentation, and lint-friendly structures
Module 2: NumPy, SciPy, and Data Handling
• NumPy foundations for vectorised computing
Executing efficient array operations and performance-aware coding
• Indexing, slicing, broadcasting, and shapes
Ensuring safe tensor manipulation and shape reasoning
• Linear algebra essentials with NumPy and SciPy
Performing stable matrix operations and decompositions used in ML
• Deep dive into SciPy
Covering statistics, optimisation, curve fitting, and sparse matrices
• Pandas for tabular ML data
Cleaning, joining, aggregating, and preparing datasets
• Deep dive into scikit-learn
Mastering the estimator interface, pipelines, and reproducible workflows
• Visualisation essentials
Creating diagnostic plots for data exploration and model behavior analysis
Module 3: Programming Patterns for Building ML Applications
• Transitioning from notebook to maintainable project
Refactoring exploratory code into structured packages
• Configuration management
Implementing externalized parameters and startup validation
• Logging, warnings, and observability
Establishing structured logging for debuggable ML systems
• Reusable components with OOP and composition
Designing extensible transformers and predictors
• Practical design patterns
Applying Pipeline, Factory or Registry, Strategy, and Adapter patterns
• Data validation and schema checks
Preventing silent data issues
• Performance and profiling
Identifying bottlenecks and applying optimization 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 foundations
Establishing train/validation splits, honest cross-validation, and business-aligned metrics
• Advanced tabular ML
Utilizing regularized GLMs, tree ensembles, and leakage-free preprocessing
• Calibration and uncertainty
Applying Platt scaling, isotonic regression, bootstrap, and conformal prediction
• Classical NLP methods
Understanding tokenization trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modelling
Covering LDA fundamentals and practical limitations
• Classical computer vision
Implementing HOG, PCA, and feature-based pipelines
• Error analysis
Detecting bias, label noise, and spurious correlations
• Hands-on labs
Building a leakage-proof tabular pipeline
Comparing and interpreting text baselines
Analyzing classical vision baselines with structured failure analysis
Module 5: Neural Networks for Tabular, Text, and Image Data
• Training loop mastery
Crafting clean PyTorch loops with AMP, clipping, and reproducibility
• Optimisation and regularisation
Mastering initialization, normalization, optimizers, and schedulers
• Mixed precision and scaling
Implementing gradient accumulation and checkpointing strategies
• Tabular neural networks
Using categorical embeddings, feature crosses, and ablation studies
• Text neural networks
Utilizing embeddings, CNNs, BiLSTM or GRU, and sequence handling
• Vision neural networks
Exploring CNN fundamentals and ResNet-style architectures
• Hands-on labs
Building a reusable training framework
Comparing Tabular NN vs boosting
Conducting CNN experiments with augmentation and scheduling
Module 6: Advanced Neural Architectures
• Transfer learning strategies
Employing 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
Managing temporal splits and detecting covariate shift
• PEFT and efficiency techniques
Navigating trade-offs in LoRA, distillation, and quantization
• Hands-on labs
Fine-tuning a pretrained text transformer
Fine-tuning a pretrained vision model
Comparing tabular transformers vs GBDT
Module 7: Generative AI Systems
• Prompting fundamentals
Utilizing structured prompting and controlled generation
• LLM foundations
Understanding tokenization, instruction tuning, and hallucination mitigation
• Retrieval-Augmented Generation (RAG)
Covering chunking, embeddings, hybrid search, and evaluation metrics
• Fine-tuning strategies
Applying LoRA and QLoRA with data quality controls
• Diffusion models
Gaining intuition for latent diffusion and practical adaptation
• Synthetic tabular data
Using CTGAN and addressing 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
Implementing observe, plan, act, reflect, and persist cycles
• Agent architectures
Exploring ReAct, plan-and-execute, and multi-agent coordination
• Memory management
Utilizing episodic, semantic, and scratchpad approaches
• Tool integration and safety
Establishing tool contracts, sandboxing, and prompt injection defenses
• Evaluation frameworks
Implementing 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 tailored 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
