ai-engineer
AI Engineer syllabus
The full 18 week document: prerequisites, 4 phases, 7 modules with every named method, and 3 capstone briefs.
Reference
Everything that exists, in one place
An index rather than a library. There is no blog and there are no downloadable guides, so this page does not pretend otherwise. What it does have is the real material: the published syllabi, the roadmaps, every project brief, and a full index of the methods taught.
The documents
The published syllabi
Both are readable in full on this site, without giving us an email address. Registering interest gets the complete document sent to you, and a short call about whether the track fits.
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ml-engineer
Machine Learning Engineer syllabus
The full 18 week document: prerequisites, 5 phases, 8 modules with every named method, and 2 capstone briefs.
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4 tracks
The other four
Cloud Engineering and DevOps, Cybersecurity, Data Engineering and Analytics, and Web Development have no syllabus yet, so there is nothing here to send. Registering interest is how you hear when one is written.
Also on this site
Roadmaps, briefs and support
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9
Phase roadmaps
Week-by-week phases for both published tracks, each ending in a checkpoint that is a portfolio artifact rather than a quiz. They live on the track pages.
Open the tracks -
20
Project briefs
Every capstone and module project, with the deliverables each one is assessed on. Filterable by track.
Open the briefs -
4
Career support areas
Portfolio review, interview preparation, profile support and job search guidance, described by what happens in each.
Open career support
Before you start
What each published track assumes
Where you learned these does not matter. Having them does, because the first weeks move quickly.
AI Engineer
- Python, APIs, Git, SQL, JSON, and REST fundamentals
- Core machine learning concepts and model evaluation basics
- Comfort reading documentation for fast-moving AI frameworks
- Ability to build several portfolio applications with deployment-ready code
Machine Learning Engineer
- Python, SQL, Git, and Linux basics
- Algebra, probability, statistics, and optimization fundamentals
- Ability to complete weekly coding projects and maintain a GitHub portfolio
- Comfort with notebooks, source-code files, documentation, and reproducible environments
A to Z
Every method and tool taught
All 111 named techniques and tools across the 15 published modules, with the track that teaches each. If something is not on this list, it is not in a syllabus.
A
- A/B testing ai-engineer
- AdaBoost ml-engineer
- Adam optimizer concept ml-engineer
- Advanced RAG ai-engineer
- Adversarial evaluation ai-engineer
- Agentic RAG ai-engineer
- Approximate Nearest Neighbor ai-engineer
B
- Bayesian inference basics ml-engineer
- Bayesian Optimization ml-engineer
- Beam Search ai-engineer
- BM25 ai-engineer
C
- Caching policies ai-engineer
- Canary deployment ai-engineer
- CatBoost Classifier ml-engineer
- CatBoost Regressor ml-engineer
- Circuit breaker pattern ai-engineer
- CNN ai-engineer
- ColBERT-style late interaction ai-engineer
- Corrective RAG ai-engineer
- Cosine Similarity ai-engineer
- Cross-Encoder Reranking ai-engineer
D
- DAG workflow ai-engineer
- DBSCAN ml-engineer
- Decision Tree Classifier ml-engineer
- Decision Tree Regressor ml-engineer
- Dimensionality reduction: PCA ml-engineer
- Dot Product Similarity ai-engineer
E
- ElasticNet ml-engineer
- Euclidean and L2 Distance ai-engineer
- Extra Trees Classifier ml-engineer
F
- Feature selection: mutual information, chi-square, ANOVA F-test ml-engineer
G
- Gaussian Mixture Models ml-engineer
- Gradient Boosting Classifier ml-engineer
- Gradient Boosting Regressor ml-engineer
- Gradient Descent ml-engineer
- GraphRAG ai-engineer
- Grid Search ml-engineer
H
- HDBSCAN ml-engineer
- Hierarchical Clustering ml-engineer
- HNSW ai-engineer
- Hybrid Dense and Sparse Retrieval ai-engineer
- HyDE ai-engineer
I
- Imputation: mean, median, mode, KNN imputation ml-engineer
- Isolation Forest ml-engineer
- Isotonic Regression ml-engineer
- IVF and Inverted File Index ai-engineer
J
- Jailbreak test suites ai-engineer
K
- K-Means ai-engineerml-engineer
- K-Nearest Neighbors ml-engineer
- KNN Regressor ml-engineer
L
- Lasso Regression ml-engineer
- LightGBM Classifier ml-engineer
- LightGBM Regressor ml-engineer
- LIME basics ml-engineer
- Linear and Logistic Regression ai-engineer
- Linear Regression ml-engineer
- Local Outlier Factor ml-engineer
- Logistic Regression ml-engineer
M
- Maximal Marginal Relevance ai-engineer
- Maximum Likelihood Estimation ml-engineer
- Mini-batch Gradient Descent ml-engineer
- MiniBatch K-Means ml-engineer
- Multi-Agent Workflow ai-engineer
- Multi-Head Attention ai-engineer
N
- Naive Bayes ml-engineer
- Naive RAG ai-engineer
O
- One-Class SVM ml-engineer
- Optuna and TPE ml-engineer
P
- Partial Dependence Plot ml-engineer
- PCA ai-engineerml-engineer
- Permutation Importance ml-engineer
- Plan-and-Execute Agent ai-engineer
- Platt Scaling ml-engineer
- Policy classifiers ai-engineer
- Product Quantization ai-engineer
- Prompt-injection detection heuristics ai-engineer
Q
- Query Expansion ai-engineer
R
- Random Forest ai-engineer
- Random Forest Classifier ml-engineer
- Random Forest Regressor ml-engineer
- Randomized Search ml-engineer
- Rate limiting algorithms ai-engineer
- ReAct Agent ai-engineer
- Reciprocal Rank Fusion ai-engineer
- Redaction methods for personal data ai-engineer
- Reflection Agent ai-engineer
- Ridge Regression ml-engineer
- RNN, LSTM, GRU ai-engineer
S
- Sampling: random, stratified, SMOTE awareness ml-engineer
- Scalar Quantization ai-engineer
- Self-Attention ai-engineer
- Self-RAG ai-engineer
- Semantic cache ai-engineer
- SHAP basics ml-engineer
- State Machine Graph ai-engineer
- Stochastic Gradient Descent ml-engineer
- Supervisor and Router Agent ai-engineer
- Support Vector Machine ml-engineer
- Support Vector Regression ml-engineer
- SVM ai-engineer
T
- t-SNE ml-engineer
- Temperature Scaling ai-engineer
- Tool-Calling Agent ai-engineer
- Top-k Sampling ai-engineer
- Top-p and Nucleus Sampling ai-engineer
- Transformer ai-engineer
- Tree-of-Thought style search concepts ai-engineer
U
- UMAP ml-engineer
X
- XGBoost Classifier ml-engineer
- XGBoost Regressor ml-engineer
- XGBoost, LightGBM, CatBoost ai-engineer
The syllabus is the thing worth having
Register interest and we will send the complete week-by-week document for the track you are considering. No payment details, and nothing to install.