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 prevalent 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
Employ typed containers for configuration, examples, and results
• Decorators and context managers
Apply patterns for timing, caching, logging, and resource-safe execution
• Working with files and paths
Master robust dataset handling and 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: Numerical Python, SciPy and Data Handling
• NumPy foundations for vectorised computing
Perform efficient array operations and practice performance-aware coding
• Indexing, slicing, broadcasting and shapes
Ensure safe tensor manipulation and accurate shape reasoning
• Linear algebra essentials with NumPy and SciPy
Execute stable matrix operations and decompositions essential for ML
• SciPy deep dive
Cover statistics, optimisation, curve fitting, and sparse matrices
• Pandas for tabular ML data
Clean, join, aggregate, and prepare datasets using Pandas
• scikit-learn deep dive
Utilize the estimator interface, pipelines, and reproducible workflows
• Visualisation 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, maintainable packages
• Configuration management
Manage externalized parameters and validate startup configurations
• Logging, warnings and observability
Implement structured logging for debuggable ML systems
• Reusable components with OOP and composition
Design extensible transformers and predictors using OOP principles
• Practical design patterns
Apply Pipeline, Factory or Registry, Strategy, and Adapter patterns
• Data validation and schema checks
Prevent silent data issues through rigorous validation
• Performance and profiling
Identify bottlenecks and apply appropriate 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
Understand train and validation splits, honest cross-validation, and business-aligned metrics
• Advanced tabular ML
Implement regularised GLMs, tree ensembles, and leakage-free preprocessing
• Calibration and uncertainty
Apply Platt scaling, isotonic regression, bootstrap, and conformal prediction
• Classical NLP methods
Analyze tokenisation trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modelling
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
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
Build clean PyTorch loops with AMP, clipping, and reproducibility in mind
• Optimisation and regularisation
Manage initialisation, normalisation, optimisers, and schedulers
• Mixed precision and scaling
Implement gradient accumulation and checkpointing strategies
• Tabular neural networks
Utilize categorical embeddings, feature crosses, and ablation studies
• Text neural networks
Work with embeddings, CNNs, BiLSTM or GRU, and sequence handling
• Vision neural networks
Understand 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 and 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
Study TabTransformer, FT-Transformer, and Deep and Cross networks
• Time series considerations
Address temporal splits and covariate shift detection
• PEFT and efficiency techniques
Evaluate 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
Master structured prompting and controlled generation techniques
• LLM foundations
Understand tokenisation, instruction tuning, and hallucination mitigation
• Retrieval-Augmented Generation
Implement chunking, embeddings, hybrid search, and evaluation metrics
• Fine-tuning strategies
Apply LoRA and QLoRA with strict data quality controls
• Diffusion models
Gain intuition for latent diffusion and practical adaptation methods
• Synthetic tabular data
Utilize CTGAN while considering 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
Design loops that observe, plan, act, reflect, and persist
• Agent architectures
Explore ReAct, plan-and-execute, and multi-agent coordination models
• Memory management
Implement episodic, semantic, and scratchpad memory approaches
• Tool integration and safety
Establish tool contracts, sandboxing, and prompt injection defences
• Evaluation frameworks
Utilize 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 must possess a practical working knowledge of Python programming.
This programme is designed for technical professionals ranging from intermediate to advanced levels.
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.
