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 common 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
Use typed containers for configuration, examples, and results
• Decorators and context managers
Implement patterns for timing, caching, logging, and resource-safe execution
• Working with files and paths
Handle datasets robustly and manage 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 maintain a lint-friendly structure
Module 2: Numerical Python, SciPy and Data Handling
• NumPy foundations for vectorised computing
Perform efficient array operations and code with performance in mind
• Indexing, slicing, broadcasting, and shapes
Manipulate tensors safely and reason about shapes
• Linear algebra essentials with NumPy and SciPy
Execute stable matrix operations and decompositions used in ML
• SciPy deep dive
Cover 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
• Visualization 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
Externalize parameters and implement startup validation
• Logging, warnings, and observability
Use structured logging for debuggable ML systems
• Reusable components with OOP and composition
Design extensible transformers and predictors
• Practical design patterns
Apply Pipeline, Factory, Registry, Strategy, and Adapter patterns
• Data validation and schema checks
Prevent silent data issues
• 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
Manage 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
Implement Platt scaling, isotonic regression, bootstrap, and conformal prediction
• Classical NLP methods
Explore tokenization trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modelling
Understand LDA fundamentals and practical limitations
• Classical computer vision
Use 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
Implement clean PyTorch loops with AMP, clipping, and reproducibility
• Optimization and regularization
Manage initialization, normalization, optimizers, and schedulers
• Mixed precision and scaling
Use gradient accumulation and checkpointing strategies
• Tabular neural networks
Work with categorical embeddings, feature crosses, and ablation studies
• Text neural networks
Utilize embeddings, CNNs, BiLSTM or GRU, and sequence handling
• Vision neural networks
Cover 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
Use freeze/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
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-tune pretrained text transformer
Fine-tune pretrained vision model
Tabular transformer vs GBDT comparison
Module 7: Generative AI Systems
• Prompting fundamentals
Use structured prompting and control generation
• LLM foundations
Cover tokenization, instruction tuning, and hallucination mitigation
• Retrieval-Augmented Generation
Implement 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 consider privacy implications
• 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
Use episodic, semantic, and scratchpad approaches
• Tool integration and safety
Define tool contracts, implement sandboxing, and defend against prompt injection
• Evaluation frameworks
Use 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 working knowledge of Python programming.
This course is designed 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
