Course Outline
Module 1: Essential Python for Machine Learning Workflows
• Program introduction and environment configuration
Define objectives and establish a reproducible Python ML workspace
• Python language fundamentals (intensive 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 utilities
Implement transformations using comprehensions and higher-order functions
• Object-oriented Python for ML developers
Classes, methods, composition, and practical design considerations
• dataclasses and lightweight modeling
Typed containers for configuration, examples, and results
• Decorators and context managers
Patterns for timing, caching, logging, and resource-safe execution
• File and path management
Robust dataset handling and serialization formats
• Exceptions and defensive programming
Writing ML scripts that fail safely and transparently
• Modules, packages, and project structure
Organizing 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 vectorized 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, optimization, 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
• Visualization essentials
Diagnostic plots for data exploration and model behavior
Module 3: Programming Patterns for Building ML Applications
• From notebook to maintainable project
Refactoring exploratory code into structured packages
• Configuration management
Externalized 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 optimization 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 Data
• Evaluation foundations
Train and validation splits, honest cross-validation, and business-aligned metrics
• Advanced tabular ML
Regularized GLMs, tree ensembles, and leakage-free preprocessing
• Calibration and uncertainty
Platt scaling, isotonic regression, bootstrap, and conformal prediction
• Classical NLP methods
Tokenization trade-offs, TF-IDF, linear models, and Naive Bayes
• Topic modeling
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 Data
• Training loop mastery
Clean PyTorch loops with AMP, clipping, and reproducibility
• Optimization and regularization
Initialization, normalization, optimizers, 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 quantization 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
Tokenization, 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 defenses
• 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 are expected to possess a functional understanding of Python programming.
This program is designed for technical professionals at 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.
