Certified AI Excellence Engineer (CAIEE).

What you'll graduate with

Walk away with real proof of your skills.

Program curriculum

Aiexl-Certified AI Engineer Program.

Chapter-level curriculum breakdown. Each of the 10 modules is sequenced so learners build on prior concepts, with core topics and estimated time allocations to pace a 12-week schedule.

1. M01

   Python & Software Engineering for AI

   Weeks 1–2

   • 1.1

     Python Fundamentals for AI Engineers

     Variables, data types, control flow, functions, list/dict comprehensions, error handling. Focus on patterns AI engineers use daily (data wrangling idioms, file I/O).

   • 1.2

     Object-Oriented Programming & Clean Code

     Classes, inheritance, encapsulation, modular code design, writing reusable/testable Python for ML pipelines.

   • 1.3

     Working with APIs & JSON

     REST API basics, requests library, parsing/serializing JSON, authentication (API keys, tokens), rate limiting.

   • 1.4

     Git, GitHub & Dev Environment Setup

     Git basics (init, commit, branch, merge, PRs), GitHub collaboration workflow, VS Code setup, virtual environments (venv/conda), dependency management.

   • 1.5

     Bringing It Together: Software Engineering Best Practices for AI

     Consolidating clean code, version control, and API integration habits that carry through the rest of the program.

2. M02

   SQL & Data Fundamentals

   Weeks 2–3

   • 2.1

     SQL Foundations

     SELECT, WHERE, GROUP BY, ORDER BY, aggregate functions, filtering and sorting large datasets.

   • 2.2

     Joins & Relational Data Modeling

     Inner/outer/left/right joins, foreign keys, normalized schema design, entity-relationship thinking.

   • 2.3

     Window Functions & Advanced Querying

     RANK, ROW_NUMBER, PARTITION BY, running totals, subqueries and CTEs for analytical queries.

   • 2.4

     ETL Concepts & Data Pipelines

     Extract-Transform-Load patterns, batch vs. streaming, data cleaning/validation, pipeline orchestration basics.

   • 2.5

     Data Warehousing Fundamentals

     Star/snowflake schemas, warehousing vs. OLTP, designing data structures for analytics at scale.

3. M03

   Mathematics & Machine Learning

   Weeks 3–5

   • 3.1

     Linear Algebra for Machine Learning

     Vectors, matrices, matrix multiplication, eigenvalues/eigenvectors — intuition over proofs, tied to ML operations.

   • 3.2

     Statistics & Probability Foundations

     Descriptive statistics, distributions, hypothesis testing, Bayes' theorem, correlation vs. causation.

   • 3.3

     Regression Fundamentals

     Linear and logistic regression, cost functions, gradient descent, assumptions and diagnostics.

   • 3.4

     Classification & Feature Engineering

     Classification algorithms overview, encoding categorical data, scaling, handling missing values, feature selection.

   • 3.5

     Model Evaluation Techniques

     Train/test splits, cross-validation, precision/recall/F1/ROC-AUC, and how to judge model quality rigorously.

4. M04

   Advanced ML & Explainable AI

   Weeks 5–6

   • 4.1

     Gradient Boosting: XGBoost & LightGBM

     Boosting theory, tree-based ensembles, XGBoost/LightGBM implementation and tuning basics.

   • 4.2

     CatBoost & Ensemble Strategies

     CatBoost for categorical-heavy data, bagging vs. boosting, stacking and blending ensembles.

   • 4.3

     Hyperparameter Tuning

     Grid search, random search, Bayesian optimization (Optuna), cross-validation strategies for tuning.

   • 4.4

     Explainable AI: SHAP & LIME

     Model interpretability principles, SHAP values, LIME explanations, global vs. local interpretability.

   • 4.5

     Responsible AI Principles

     Bias/fairness in ML, regulatory considerations, and building models that are transparent and defensible.

5. M05

   Deep Learning & Neural Networks

   Weeks 6–8

   • 5.1

     Neural Network Foundations

     Perceptrons, activation functions, forward/backpropagation, loss functions — built from first principles.

   • 5.2

     TensorFlow & PyTorch Essentials

     Tensors, autograd/computation graphs, building and training a model in both frameworks.

   • 5.3

     Optimizers & Training Dynamics

     SGD, Adam, RMSprop, learning rate scheduling, batch size effects, monitoring training curves.

   • 5.4

     Regularization Techniques

     Dropout, batch normalization, weight decay, early stopping, diagnosing overfitting/underfitting.

   • 5.5

     CNN Basics

     Convolution, pooling, and foundational CNN architecture as a bridge into computer vision.

6. M06

   Computer Vision

   Weeks 8–9

   • 6.1

     Image Processing with OpenCV

     Image manipulation, filtering, edge detection, contours, preprocessing pipelines for CV models.

   • 6.2

     CNN Architectures: ResNet & EfficientNet

     Deeper architectures, residual connections, compound scaling, choosing architectures for constraints.

   • 6.3

     Object Detection with YOLO

     Detection vs. classification, YOLO architecture, bounding boxes, non-max suppression, training a detector.

   • 6.4

     Transfer Learning for Vision

     Pretrained models, fine-tuning vs. feature extraction, domain adaptation for small datasets.

   • 6.5

     Applied Computer Vision Workflows

     Putting preprocessing, architecture choice, and transfer learning together into a full CV workflow.

7. M07

   NLP, Transformers & LLMs

   Weeks 9–11

   • 7.1

     Word Embeddings & Text Representation

     Bag-of-words, TF-IDF, Word2Vec/GloVe, tokenization strategies, contextual vs. static embeddings.

   • 7.2

     Attention Mechanism & Transformer Architecture

     Self-attention, multi-head attention, positional encoding, encoder-decoder structure — the architecture behind modern LLMs.

   • 7.3

     BERT & Encoder Models

     Masked language modeling, fine-tuning BERT for classification/NER, encoder-only use cases.

   • 7.4

     GPT & Generative Language Models

     Autoregressive generation, decoder-only architecture, how GPT-style models are trained and prompted.

   • 7.5

     Hugging Face Ecosystem

     Transformers library, pipelines, model hub, and practical fine-tuning workflows.

8. M08

   Prompt Engineering & RAG Systems

   Weeks 11–13

   • 8.1

     Prompt Engineering Fundamentals

     Zero-shot/few-shot prompting, chain-of-thought, prompt templates, common failure modes and fixes.

   • 8.2

     Working with OpenAI APIs

     Chat completions, function/tool calling, streaming responses, cost and token management.

   • 8.3

     Embeddings & Vector Databases

     Text embeddings for semantic search, vector DB options (Pinecone, Chroma, FAISS), similarity search.

   • 8.4

     Building with LangChain & LlamaIndex

     Chains, retrievers, document loaders, indexing strategies for retrieval-augmented workflows.

   • 8.5

     RAG System Design

     Full RAG pipeline design (chunking, retrieval, generation) and how to evaluate retrieval quality.

9. M09

   AI Agents & Agentic Systems

   Weeks 13–15

   • 9.1

     Foundations of AI Agents & Tool Calling

     What makes a system 'agentic,' tool/function calling patterns, planning and reasoning loops.

   • 9.2

     Agent Memory & State Management

     Short-term vs. long-term memory, conversation state, persistence strategies for agents.

   • 9.3

     Multi-Agent Systems Design

     Agent roles, orchestration patterns, communication protocols between agents.

   • 9.4

     Building Agents with LangGraph, CrewAI & AutoGen

     Hands-on comparison of frameworks and when to use each for different agentic workflows.

   • 9.5

     Agentic Systems in Practice

     Design patterns for reliable, tool-using, multi-step agent workflows.

10. M10

    MLOps, LLMOps & Cloud Deployment

    Weeks 15–18

    • 10.1

      Building APIs with FastAPI

      REST API design for ML models, request/response schemas (Pydantic), serving predictions.

    • 10.2

      Containerization with Docker

      Dockerfiles, images vs. containers, packaging an ML app for reproducible deployment.

    • 10.3

      Cloud Deployment: AWS & GCP

      Core services for ML hosting (EC2/S3/SageMaker, GCP equivalents), deployment basics.

    • 10.4

      CI/CD, MLflow & Monitoring

      Automated testing/deployment pipelines, experiment tracking with MLflow, model/data drift monitoring.

    • 10.5

      Security in Production AI Systems

      API security, secrets management, rate limiting, and hardening deployed AI systems.