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
Apply classes, methods, composition, and practical design decisions.
• dataclasses and lightweight modelling
Create typed containers for configuration, examples, and results.
• Decorators and context managers
Implement timing, caching, logging, and resource-safe execution patterns.
• Working with files and paths
Manage robust dataset handling and serialization formats.
• Exceptions and defensive programming
Write ML scripts that fail safely and transparently.
• Modules, packages and project structure
Organize reusable ML codebases effectively.
• Typing and code quality
Apply type hints, documentation, and lint-friendly structures.
Module 2: Numerical Python, SciPy and Data Handling
• NumPy foundations for vectorised computing
Execute efficient array operations and performance-aware coding.
• Indexing, slicing, broadcasting and shapes
Manipulate tensors safely and reason about shapes.
• Linear algebra essentials with NumPy and SciPy
Perform stable matrix operations and decompositions essential for ML.
• SciPy deep dive
Explore statistics, optimization, curve fitting, and sparse matrices.
• Pandas for tabular ML data
Clean, join, aggregate, and prepare datasets.
• scikit-learn deep dive
Master the estimator interface, pipelines, and reproducible workflows.
• Visualisation essentials
Create diagnostic plots for data exploration and model behavior analysis.
Module 3: Programming Patterns for Building ML Applications
• From notebook to maintainable project
Refactor exploratory code into structured packages.
• Configuration management
Manage externalized parameters and validate startup conditions.
• 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 optimization 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
Establish train/validation splits, honest cross-validation, and business-aligned metrics.
• Advanced tabular ML
Utilize regularized GLMs, tree ensembles, and leakage-free preprocessing.
• Calibration and uncertainty
Apply Platt scaling, isotonic regression, bootstrap, and conformal prediction.
• Classical NLP methods
Navigate tokenization trade-offs, TF-IDF, linear models, and Naive Bayes.
• Topic modelling
Understand 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
Create clean PyTorch loops with AMP, clipping, and reproducibility.
• Optimisation and regularisation
Manage initialization, normalization, optimizers, 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
Employ embeddings, CNNs, BiLSTM or GRU, and sequence handling.
• Vision neural networks
Master 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
Apply freeze/unfreeze patterns and discriminative learning rates.
• Transformer architectures for text
Understand self-attention internals and fine-tuning approaches.
• Vision backbones and dense prediction
Explore ResNet, EfficientNet, Vision Transformers, and U-Net concepts.
• Advanced tabular architectures
Utilize TabTransformer, FT-Transformer, and Deep and Cross networks.
• Time series considerations
Handle temporal splits and detect covariate shift.
• PEFT and efficiency techniques
Navigate LoRA, distillation, and quantization 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 tokenization, instruction tuning, and hallucination mitigation.
• Retrieval-Augmented Generation
Manage chunking, embeddings, hybrid search, and evaluation metrics.
• Fine-tuning strategies
Apply LoRA and QLoRA with rigorous data quality controls.
• Diffusion models
Grasp latent diffusion intuition and practical adaptation.
• Synthetic tabular data
Utilize CTGAN while addressing 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
Enforce tool contracts, sandboxing, and prompt injection defenses.
• Evaluation frameworks
Deploy replayable traces, task suites, and regression testing.
• 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 are expected to possess a functional understanding of Python programming.
This program 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
