Course Outline
Module 1: Core Python for ML Workflows
• Course kickoff and environment setup
Align objectives and set up a reproducible Python ML workspace
• Python language essentials (fast-track)
Review syntax, control flow, functions and patterns commonly used in ML codebases
• Data structures for ML
Lists, dictionaries, sets and tuples for features, labels and metadata
• Comprehensions and functional tools
Express transformations using comprehensions and higher-order functions
• Object-oriented Python for ML developers
Classes, methods, composition and practical design decisions
• dataclasses and lightweight modelling
Typed containers for configuration, examples and results
• Decorators and context managers
Timing, caching, logging and resource-safe execution patterns
• Working with files and paths
Robust dataset handling and serialization formats
• Exceptions and defensive programming
Writing ML scripts that fail safely and transparently
• Modules, packages and project structure
Organising reusable ML codebases
• Typing and code quality
Type hints, documentation and lint-friendly structure
Module 2: Numerical Python, SciPy and Data Handling
• NumPy foundations for vectorised computing
Efficient array operations and performance-aware coding
• Indexing, slicing, broadcasting and shapes
Safe tensor manipulation and shape reasoning
• Linear algebra essentials with NumPy and SciPy
Stable matrix operations and decompositions used in ML
• SciPy deep dive
Statistics, optimisation, curve fitting and sparse matrices
• Pandas for tabular ML data
Cleaning, joining, aggregating and preparing datasets
• scikit-learn deep dive
Estimator interface, pipelines and reproducible workflows
• Visualisation essentials
Diagnostic plots for data exploration and model behaviour
Module 3: Programming Patterns for Building ML Applications
• From notebook to maintainable project
Refactoring exploratory code into structured packages
• Configuration management
Externalised parameters and startup validation
• Logging, warnings and observability
Structured logging for debuggable ML systems
• Reusable components with OOP and composition
Designing extensible transformers and predictors
• Practical design patterns
Pipeline, Factory or Registry, Strategy and Adapter patterns
• Data validation and schema checks
Preventing silent data issues
• Performance and profiling
Identifying bottlenecks and applying optimisation techniques
• Model I O and inference interfaces
Safe persistence and clean prediction interfaces
• End-to-end mini build
Production-style ML pipeline with configuration and logging
Module 4: Statistical Learning for Tabular, Text and Image
• Evaluation foundations
Train and validation splits, honest cross-validation and business-aligned metrics
• Advanced tabular ML
Regularised GLMs, tree ensembles and leakage-free preprocessing
• Calibration and uncertainty
Platt scaling, isotonic regression, bootstrap and conformal prediction
• Classical NLP methods
Tokenisation trade-offs, TF-IDF, linear models and Naive Bayes
• Topic modelling
LDA fundamentals and practical limitations
• Classical computer vision
HOG, PCA and feature-based pipelines
• Error analysis
Bias detection, 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
Clean PyTorch loops with AMP, clipping and reproducibility
• Optimisation and regularisation
Initialisation, normalisation, optimisers and schedulers
• Mixed precision and scaling
Gradient accumulation and checkpointing strategies
• Tabular neural networks
Categorical embeddings, feature crosses and ablation studies
• Text neural networks
Embeddings, CNNs, BiLSTM or GRU and sequence handling
• Vision neural networks
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
Freeze and unfreeze patterns, discriminative learning rates
• Transformer architectures for text
Self-attention internals and fine-tuning approaches
• Vision backbones and dense prediction
ResNet, EfficientNet, Vision Transformers and U-Net concepts
• Advanced tabular architectures
TabTransformer, FT-Transformer and Deep and Cross networks
• Time series considerations
Temporal splits and covariate shift detection
• PEFT and efficiency techniques
LoRA, distillation and quantisation 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
Structured prompting and controlled generation
• LLM foundations
Tokenisation, instruction tuning and hallucination mitigation
• Retrieval-Augmented Generation
Chunking, embeddings, hybrid search and evaluation metrics
• Fine-tuning strategies
LoRA and QLoRA with data quality controls
• Diffusion models
Latent diffusion intuition and practical adaptation
• Synthetic tabular data
CTGAN and 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
Observe, plan, act, reflect and persist
• Agent architectures
ReAct, plan-and-execute and multi-agent coordination
• Memory management
Episodic, semantic and scratchpad approaches
• Tool integration and safety
Tool contracts, sandboxing and prompt injection defences
• Evaluation frameworks
Replayable traces, task suites and regression testing
• MCP and protocol-based interoperability
Designing 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 should have a working knowledge of Python programming.
This programme is intended for intermediate to advanced technical professionals.
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
