Course Outline
Module 1: Core Python for Machine Learning Workflows
• Course kickoff and environment setup
Align objectives and establish a reproducible Python ML workspace
• Essential Python concepts (fast-track)
Review syntax, control flow, functions, and patterns prevalent in ML codebases
• Data structures for machine learning
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 modeling
Using typed containers for configuration, examples, and results
• Decorators and context managers
Applying patterns for timing, caching, logging, and resource-safe execution
• Handling 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: Numerical Python, SciPy, and Data Handling
• NumPy foundations for vectorized computing
Mastering 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
Exploring statistics, optimization, curve fitting, and sparse matrices
• Pandas for tabular ML data
Cleaning, joining, aggregating, and preparing datasets
• Deep dive into scikit-learn
Navigating the estimator interface, pipelines, and reproducible workflows
• Visualization essentials
Creating diagnostic plots for data exploration and model behavior analysis
Module 3: Programming Patterns for Building ML Applications
• Transitioning from notebooks to maintainable projects
Refactoring exploratory code into structured packages
• Configuration management
Managing externalized parameters and startup validation
• Logging, warnings, and observability
Implementing structured logging for debuggable ML systems
• Building 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 through rigorous validation
• 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 machine learning
Utilizing regularized GLMs, tree ensembles, and leakage-free preprocessing
• Calibration and uncertainty
Employing Platt scaling, isotonic regression, bootstrap, and conformal prediction
• Classical NLP methods
Understanding tokenization trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modeling
Grasping LDA fundamentals and practical limitations
• Classical computer vision
Working with 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
• Mastering the training loop
Implementing clean PyTorch loops with AMP, clipping, and reproducibility features
• Optimization and regularization
Managing initialization, normalization, optimizers, and schedulers
• Mixed precision and scaling
Utilizing gradient accumulation and checkpointing strategies
• Neural networks for tabular data
Using categorical embeddings, feature crosses, and ablation studies
• Neural networks for text data
Incorporating embeddings, CNNs, BiLSTMs or GRUs, and sequence handling
• Neural networks for vision data
Focusing on CNN fundamentals and ResNet-style architectures
• Hands-on labs
Building a reusable training framework
Comparing tabular NNs with boosting methods
Conducting experiments with CNN augmentation and scheduling
Module 6: Advanced Neural Architectures
• Transfer learning strategies
Applying freeze/unfreeze patterns and discriminative learning rates
• Transformer architectures for text
Exploring self-attention internals and fine-tuning approaches
• Vision backbones and dense prediction
Understanding ResNet, EfficientNet, Vision Transformers, and U-Net concepts
• Advanced tabular architectures
Utilizing TabTransformer, FT-Transformer, and Deep and Cross networks
• Time series considerations
Performing temporal splits and detecting covariate shift
• PEFT and efficiency techniques
Examining LoRA, distillation, and quantization trade-offs
• Hands-on labs
Fine-tuning a pretrained text transformer
Fine-tuning a pretrained vision model
Comparing tabular transformers with GBDT models
Module 7: Generative AI Systems
• Prompting fundamentals
Implementing structured prompting and controlled generation techniques
• LLM foundations
Understanding tokenization, instruction tuning, and hallucination mitigation
• Retrieval-Augmented Generation (RAG)
Mastering chunking, embeddings, hybrid search, and evaluation metrics
• Fine-tuning strategies
Applying LoRA and QLoRA with rigorous data quality controls
• Diffusion models
Gaining intuition for latent diffusion and practical adaptation methods
• Synthetic tabular data
Utilizing CTGAN and addressing privacy considerations
• Hands-on labs
Developing 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 methods
• Memory management
Utilizing episodic, semantic, and scratchpad approaches
• Tool integration and safety
Establishing tool contracts, sandboxing, and defenses against prompt injection
• Evaluation frameworks
Using 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 practical working knowledge of Python programming.
This programme is designed for technical professionals at an intermediate to advanced level.
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
