AI Engineering Mastery: From Python Engineering to Production-Ready AI Systems Training Course

Module 1: Core Python for ML Workflows

• Program kickoff and environment setup
Align objectives and set up a reproducible Python ML workspace

• Essentials of the Python language (fast-track)
Review syntax, control flow, functions, and patterns commonly found in ML codebases

• Data structures for ML
Lists, dictionaries, sets, and tuples for handling 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 modeling
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
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

• Deep dive into SciPy
Statistics, optimization, curve fitting, and sparse matrices

• Pandas for tabular ML data
Cleaning, joining, aggregating, and preparing datasets

• Deep dive into scikit-learn
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

• 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

• 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