Kurzusleírás
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
Követelmények
Participants should have a working knowledge of Python programming.
This programme is intended for intermediate to advanced technical professionals.
Vélemények (2)
az ML-ekoszisztéma nem csak az MLFlow-t, hanem az Optunát, a HyperOps-t, valamint a Docker-t és a Docker-Compose-t is tartalmazza
Guillaume GAUTIER - OLEA MEDICAL
Kurzus - MLflow
Gépi fordítás
Nagyezték, hogy részese lehettem a Kubeflow tanfolyamnak, amelyet távollértű módon tartottak. A képzés lehetővé tette számomra, hogy megalapozzam az AWS-szal kapcsolatos ismereteimet, a K8s-t és a Kubeflow környezetében használt minden devOps eszköz alapjait, amelyek a témakör megfelelő felépítéséhez szükségesek. Szeretném köszönetet mondani Malawski Marcinnek az általa tanúsított türelmért és professzionális eljárásokért a képzés során és a legjobb gyakorlati tanácsokért. Malawski számos különböző szempontból közelítette meg a témát, különböző üzembe helyezési eszközökkel, mint például az Ansible, EKS kubectl és a Terraform. Most már egyértelműen meggyőződtem arról, hogy a megfelelő alkalmazási területre haladszom.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
Kurzus - Kubeflow
Gépi fordítás
