Course Outline
Module 1: Essential Python for Machine Learning Workflows
• Programme initiation and environment configuration
Align learning objectives and establish a reproducible Python ML workspace
• Core Python language features (accelerated review)
Review syntax, control flow, functions, and patterns prevalent in ML codebases
• Data structures for ML
Utilizing lists, dictionaries, sets, and tuples for features, labels, and metadata
• Comprehensions and functional programming tools
Implementing transformations using comprehensions and higher-order functions
• Object-oriented Python for ML developers
Working with classes, methods, composition, and practical design choices
• dataclasses and lightweight modeling
Using typed containers for configuration, examples, and results
• Decorators and context managers
Implementing timing, caching, logging, and resource-safe execution patterns
• File handling and path management
Managing datasets robustly and working with 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
Covering statistics, optimization, curve fitting, and sparse matrices
• Pandas for tabular ML data
Cleaning, joining, aggregating, and preparing datasets
• Deep dive into scikit-learn
Utilizing 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
Implementing externalized parameters and startup validation
• Logging, warnings, and observability
Using 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 proactively
• Performance profiling and optimization
Identifying bottlenecks and applying optimization techniques
• Model I/O and inference interfaces
Ensuring safe persistence and clean prediction interfaces
• End-to-end mini project
Building a production-style ML pipeline with configuration and logging
Module 4: Statistical Learning for Tabular, Text, and Image Data
• Evaluation fundamentals
Implementing train/validation splits, honest cross-validation, and business-aligned metrics
• Advanced tabular ML
Utilizing regularized GLMs, tree ensembles, and leakage-free preprocessing
• Calibration and uncertainty estimation
Applying Platt scaling, isotonic regression, bootstrap methods, and conformal prediction
• Classical NLP methods
Understanding tokenization trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modeling
Exploring LDA fundamentals and practical limitations
• Classical computer vision techniques
Implementing HOG, PCA, and feature-based pipelines
• Error analysis
Detecting bias, identifying label noise, and spotting spurious correlations
• Hands-on labs
Building a leakage-proof tabular pipeline
Comparing text baselines and interpreting results
Analyzing classical vision baselines with structured failure analysis
Module 5: Neural Networks for Tabular, Text, and Image Data
• Mastering the training loop
Clean PyTorch loops with AMP, clipping, and reproducibility measures
• Optimization and regularization techniques
Implementing initialization, normalization, optimizers, and schedulers
• Mixed precision and scaling strategies
Using gradient accumulation and checkpointing strategies
• Tabular neural networks
Working with categorical embeddings, feature crosses, and ablation studies
• Text neural networks
Utilizing embeddings, CNNs, BiLSTM/GRU architectures, and sequence handling
• Vision neural networks
Mastering CNN fundamentals and ResNet-style architectures
• Hands-on labs
Building a reusable training framework
Comparing tabular NNs vs. boosting methods
Conducting CNN experiments with augmentation and scheduling
Module 6: Advanced Neural Architectures
• Transfer learning strategies
Implementing freeze/unfreeze patterns and discriminative learning rates
• Transformer architectures for text
Understanding self-attention internals and fine-tuning approaches
• Vision backbones and dense prediction models
Exploring ResNet, EfficientNet, Vision Transformers, and U-Net concepts
• Advanced tabular architectures
Implementing TabTransformer, FT-Transformer, and Deep & Cross networks
• Time series considerations
Managing temporal splits and detecting covariate shift
• PEFT and efficiency techniques
Evaluating LoRA, distillation, and quantization trade-offs
• Hands-on labs
Fine-tuning a pretrained text transformer
Fine-tuning a pretrained vision model
Comparing tabular transformers vs. GBDT models
Module 7: Generative AI Systems
• Prompting fundamentals
Mastering structured prompting and controlled generation
• Foundations of LLMs
Understanding tokenization, instruction tuning, and hallucination mitigation
• Retrieval-Augmented Generation (RAG)
Implementing chunking, embeddings, hybrid search, and evaluation metrics
• Fine-tuning strategies
Applying LoRA and QLoRA with strict data quality controls
• Diffusion models
Gaining intuition for latent diffusion and practical adaptation techniques
• Synthetic tabular data generation
Using CTGAN while addressing privacy considerations
• Hands-on labs
Developing a production-style RAG mini-application
Validating structured output with schema enforcement
Optional experimentation with diffusion models
Module 8: AI Agents and MCP (Model Context Protocol)
• Agent loop design
Implementing observe, plan, act, reflect, and persist cycles
• Agent architectures
Designing ReAct, plan-and-execute, and multi-agent coordination systems
• Memory management
Utilizing episodic, semantic, and scratchpad memory approaches
• Tool integration and safety measures
Defining tool contracts, sandboxing techniques, and defending against prompt injection
• Evaluation frameworks
Creating replayable traces, task suites, and regression testing protocols
• 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 practical working knowledge of Python programming.
This programme is designed for technical professionals at intermediate to advanced levels.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 8000 € + VAT*
Contact us for an exact quote and to hear our latest promotions
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
