AI Engineering Mastery: From Python Engineering to Production-Ready AI Systems Training Course

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.