Course Outline
Module 1: Essential Python for Machine Learning Workflows
• Program launch and environment configuration
Align goals and establish a reproducible Python ML workspace
• Core Python concepts (accelerated review)
Review syntax, control structures, functions, and patterns prevalent in ML codebases
• Data structures for machine learning
Utilize lists, dictionaries, sets, and tuples for features, labels, and metadata
• Comprehensions and functional utilities
Implement transformations using comprehensions and higher-order functions
• Object-oriented Python for ML developers
Leverage classes, methods, composition, and practical design choices
• dataclasses and lightweight modeling
Use typed containers for configuration, examples, and results
• Decorators and context managers
Apply patterns for timing, caching, logging, and safe resource management
• Handling files and paths
Manage datasets robustly and understand 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
Implement type hints, documentation, and lint-friendly structures
Module 2: NumPy, SciPy, and Data Management
• NumPy fundamentals for vectorized computing
Master efficient array operations and performance-aware coding
• Indexing, slicing, broadcasting, and shapes
Ensure safe tensor manipulation and shape reasoning
• Linear algebra essentials with NumPy and SciPy
Perform stable matrix operations and decompositions relevant to ML
• Deep dive into SciPy
Explore statistics, optimization, curve fitting, and sparse matrices
• Pandas for tabular ML data
Clean, join, aggregate, and prepare datasets efficiently
• Deep dive into scikit-learn
Utilize the estimator interface, pipelines, and reproducible workflows
• Visualization essentials
Create diagnostic plots for data exploration and model behavior analysis
Module 3: Programming Patterns for ML Application Development
• Transitioning from notebooks to maintainable projects
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
• Building reusable components with OOP and composition
Design extensible transformers and predictors
• Practical design patterns
Implement Pipeline, Factory/Registry, Strategy, and Adapter patterns
• Data validation and schema checks
Prevent silent data issues through rigorous validation
• Performance 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
Create a production-style ML pipeline with configuration and logging
Module 4: Statistical Learning for Tabular, Text, and Image Data
• Evaluation foundations
Manage train/validation splits, cross-validation, and business-aligned metrics
• Advanced tabular ML
Apply regularized GLMs, tree ensembles, and leakage-free preprocessing
• Calibration and uncertainty estimation
Use Platt scaling, isotonic regression, bootstrap, and conformal prediction
• Classical NLP methods
Navigate tokenization trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modeling
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
Build a leakage-proof tabular pipeline
Compare and interpret text baselines
Analyze classical vision baselines with structured failure modes
Module 5: Neural Networks for Tabular, Text, and Image Data
• Mastering the training loop
Implement clean PyTorch loops with AMP, clipping, and reproducibility
• Optimization and regularization
Manage initialization, normalization, optimizers, and schedulers
• Mixed precision and scaling
Apply gradient accumulation and checkpointing strategies
• Tabular neural networks
Use categorical embeddings, feature crosses, and conduct ablation studies
• Text neural networks
Implement embeddings, CNNs, BiLSTMs/GRUs, and sequence handling
• Vision neural networks
Understand CNN fundamentals and ResNet-style architectures
• Hands-on labs
Build a reusable training framework
Compare Tabular NNs with boosting methods
Experiment with CNN augmentation and scheduling
Module 6: Advanced Neural Architectures
• Transfer learning strategies
Use 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
Implement TabTransformer, FT-Transformer, and Deep & Cross networks
• Time series considerations
Manage temporal splits and detect covariate shift
• PEFT and efficiency techniques
Navigate LoRA, distillation, and quantization trade-offs
• Hands-on labs
Fine-tune a pretrained text transformer
Fine-tune a pretrained vision model
Compare Tabular transformers with GBDT models
Module 7: Generative AI Systems
• Fundamentals of prompting
Apply structured prompting and controlled generation techniques
• LLM foundations
Understand tokenization, instruction tuning, and hallucination mitigation
• Retrieval-Augmented Generation (RAG)
Master chunking, embeddings, hybrid search, and evaluation metrics
• Fine-tuning strategies
Utilize LoRA and QLoRA with strict data quality controls
• Diffusion models
Grasp latent diffusion intuition and practical adaptation methods
• Synthetic tabular data
Implement CTGAN and address privacy considerations
• Hands-on labs
Develop a production-style RAG mini-application
Validate structured outputs with schema enforcement
Optionally experiment with diffusion models
Module 8: AI Agents and MCP
• Agent loop design
Implement observe, plan, act, reflect, and persist mechanisms
• Agent architectures
Explore ReAct, plan-and-execute, and multi-agent coordination strategies
• Memory management
Utilize episodic, semantic, and scratchpad approaches
• Tool integration and safety
Establish tool contracts, sandboxing, and defenses against prompt injection
• Evaluation frameworks
Create replayable traces, task suites, and regression tests
• MCP and protocol-based interoperability
Design MCP servers with secure tool exposure
• Hands-on labs
Build an agent from scratch
Expose tools via an MCP-style server
Create an evaluation harness with safety constraints
Requirements
Participants are expected to possess a functional understanding of Python programming.
This program is designed for technical professionals ranging from 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
