What level is the Machine Learning Engineering program?

Intermediate. Total program length is 14 Weeks (180+ Hours of combined live instruction and lab time).

What prerequisites do I need?

Python programming; Intro ML knowledge; Basic statistics & linear algebra

Does this course include hands-on lab work?

Yes. This program includes hands-on lab time in 1 cloud lab environment provisioned by AI Labs. Every student gets a personal cloud workspace plus on-prem workstation access at our Houston Training Center.

Is this delivered online or in person?

Both. The default delivery is Online lectures + Cloud lab sessions + Team projects. In-person sessions are available at our Houston Training Center for any student who prefers on-site delivery.

What roles does this program prepare me for?

Machine Learning Engineer, Applied ML Scientist, ML Platform Engineer, Data Scientist.

Do I receive a certificate at the end?

Yes. Every program ends with a capstone project and a verifiable AI Labs completion certificate. Certificates are issued via our LMS and include the capstone work as a portfolio link.

How much does the program cost and are payment plans available?

Program tuition is $2,500. Most students use our 2-installment plan (50% at enrollment, 50% midway through). Enterprise + nonprofit pricing is available — contact us for a quote.

Machine Learning Engineering

Name: Machine Learning Engineering: Build, Optimize & Deploy Intelligent Models
Price: 2500 USD
Availability: InStock

MOD 01

ML Landscape & Prerequisites Audit

Set the stage: understand the ML engineer role, benchmark your skills, and configure your tech stack.

3 hrs ▾

📘

Course Overview & the ML Engineer's Toolkit Topic

What separates an ML engineer from a data scientist; the full ML development lifecycle; tools we'll use

40 min

📖

Reading: The ML Engineering Workflow in Industry Reading

Case studies from Google, Netflix, and Airbnb on ML in production; MLOps maturity model overview

35 min

🎯

Activity: Set Up the Course Tech Stack Activity

Configure Python environment with scikit-learn, XGBoost, MLflow, FastAPI, Docker Desktop; verify setup

1 hr

📋

Prerequisites Diagnostic Assessment Quiz

Benchmark quiz covering Python, statistics, and ML basics; personalized recommendations based on results

45 min

MOD 02

Mathematics for Machine Learning

Build the linear algebra, calculus, and statistics foundations that underpin every ML algorithm.

9h 30m ▾

📘

Linear Algebra for ML: Vectors, Matrices & Operations Topic

Dot products, matrix multiplication, transpose, inverse, norms — with NumPy implementations throughout

1h 10m

🎯

Workbook: Linear Algebra in NumPy Activity

30 graded exercises connecting abstract math to concrete NumPy code and ML relevance

1h 15m

📘

Linear Algebra II: Eigenvalues, SVD & Dimensionality Topic

Eigendecomposition intuition, PCA derivation, SVD applications in recommendation systems

55 min

📘

Calculus for ML: Derivatives, Gradients & Optimization Topic

Chain rule, partial derivatives, gradient descent derivation, learning rate intuition, saddle points

1h 5m

🎯

Workbook: Gradient Descent from Scratch Activity

Implement gradient descent for linear regression step-by-step; visualize loss landscapes

1h 15m

📘

Probability & Statistics for ML Topic

Probability distributions (Normal, Binomial, Poisson), Bayes' theorem, MLE, hypothesis testing, p-values

1h 10m

📘

Statistical Thinking for Model Validation Topic

Confidence intervals, A/B testing basics, statistical significance in ML experiments, distribution shifts

55 min

🎯

Workbook: Statistics with SciPy Activity

Hypothesis tests on real datasets, distribution fitting, Bayesian vs frequentist comparison exercises

1 hr

📋

Mathematics for ML Assessment Quiz

35 questions combining conceptual understanding and applied calculation problems

45 min

MOD 03

Data Engineering for Machine Learning

Collect, clean, and engineer features from raw data into ML-ready pipelines.

13 hrs ▾

📘

Data Sources & Collection Strategies Topic

Web scraping (BeautifulSoup, requests), public datasets (Kaggle, UCI, HuggingFace), APIs, synthetic data

55 min

📘

Data Quality & Advanced Cleaning Topic

Missing data strategies (MICE, KNN imputation), outlier detection (IQR, Z-score, Isolation Forest), data validation

1h 5m

📘

Feature Engineering I: Numeric & Categorical Features Topic

Binning, log transforms, polynomial features, one-hot, ordinal, target, frequency encoding

1h 5m

📘

Feature Engineering II: Temporal, Text & Cross Features Topic

DateTime decomposition, lag features, TF-IDF basics, feature interactions, automated feature generation with Featuretools

1h 5m

💻

Exercise: Feature Engineering Challenge Exercise

Transform a raw e-commerce dataset into a feature-rich ML-ready format; measure impact on model performance

1h 15m

📘

Feature Selection & Dimensionality Reduction Topic

Filter (correlation, chi²), wrapper (RFE), embedded (LASSO), PCA, t-SNE for visualization

1 hr

📘

scikit-learn Pipelines & ColumnTransformer Topic

Building reproducible preprocessing + model pipelines; custom transformers; pipeline serialization

55 min

🔬

Lab 1: Data Quality Audit Pipeline Lab 🧪 Hands-on Lab · Intermediate

Given a deliberately messy real-world dataset, write a full audit-and-repair pipeline with automated reporting

1h 30m

🔬

Lab 2: Production-Ready Preprocessing Pipeline Lab 🧪 Hands-on Lab · Intermediate

Build a modular sklearn pipeline with custom transformers, ColumnTransformer, and unit tests

1h 40m

🛠️

Mini Project 1: Data Engineering Sprint Mini Project 🧪 Mini Project · Intermediate

End-to-end: collect data from a public API, clean, engineer features, and deliver a versioned, documented dataset

2h 30m

MOD 04

Supervised Learning — Regression & Classification

Master the workhorse algorithms of ML: linear models, SVMs, trees, and gradient boosting.

10 hrs ▾

📘

Regularization: Ridge, Lasso & ElasticNet Topic

Bias-variance tradeoff, L1 vs L2 penalties, regularization path, when to use each

55 min

📘

Support Vector Machines: Theory & Kernels Topic

Maximal margin classifier, kernel trick (RBF, polynomial), SVR, practical SVM tuning guidelines

1h 5m

📘

Tree-Based Methods: Decision Trees & Random Forests Topic

CART algorithm, pruning, bagging, feature importance, out-of-bag error, ExtraTrees

1h 5m

📘

Gradient Boosting: XGBoost, LightGBM & CatBoost Topic

Boosting mechanics, learning rate, tree depth, early stopping, GPU acceleration, categorical handling

1h 10m

📘

Handling Imbalanced Datasets Topic

SMOTE, class weights, threshold tuning, precision-recall curves, cost-sensitive learning

55 min

📘

Probabilistic & Bayesian Models Topic

Naive Bayes variants, Gaussian Processes overview, calibrated classifiers, uncertainty quantification

55 min

💻

Exercise: Winning a Kaggle-Style Competition Exercise

Compete on an internal leaderboard: feature engineering + XGBoost/LGBM to maximize a given metric

1h 30m

🔬

Lab 3: Algorithm Comparison Benchmark Lab 🧪 Hands-on Lab · Intermediate

Train 6 algorithms on the same dataset, build a comparison dashboard with metrics, learning curves, and conclusions

1h 40m

📋

Supervised Learning Deep Assessment Quiz

40 questions: algorithm selection, hyperparameter reasoning, bias-variance scenarios, code output prediction

45 min

MOD 05

Unsupervised Learning — Clustering & Dimensionality

Discover structure in unlabeled data with clustering, dimensionality reduction, and anomaly detection.

8h 20m ▾

📘

K-Means Clustering: Algorithm & Applications Topic

Lloyd's algorithm, elbow method, silhouette score, K-Means++, Mini-Batch KMeans for large datasets

55 min

📘

Hierarchical & Density-Based Clustering Topic

Agglomerative clustering, dendrograms, DBSCAN, HDBSCAN — when to use each, noise point handling

55 min

📘

Dimensionality Reduction: PCA, t-SNE & UMAP Topic

Variance explained, scree plots, t-SNE perplexity tuning, UMAP for large datasets, visualization workflows

1 hr

📘

Anomaly Detection Methods Topic

Isolation Forest, Local Outlier Factor, One-Class SVM, Autoencoder-based detection overview

50 min

🔬

Lab 4: Customer Segmentation System Lab 🧪 Hands-on Lab · Intermediate

Cluster retail customers using RFM features; visualize segments; write business interpretation for each cluster

1h 40m

🛠️

Mini Project 2: Fraud Anomaly Detection System Mini Project 🧪 Mini Project · Intermediate

Build an anomaly detection pipeline on a financial transactions dataset; threshold selection and evaluation

2h 30m

📋

Unsupervised Learning Assessment Quiz

30 questions: algorithm selection, metric interpretation, evaluation without ground truth

30 min

MOD 06

Model Evaluation, Selection & Hyperparameter Tuning

Validate models rigorously, optimize hyperparameters, and interpret what they learned.

7h 5m ▾

📘

Cross-Validation Strategies Topic

K-Fold, Stratified K-Fold, TimeSeriesSplit, Leave-One-Out, nested cross-validation for unbiased evaluation

55 min

📘

Hyperparameter Optimization Topic

Grid search, random search, Bayesian optimization (Optuna), Hyperband, practical tuning strategies

1h 5m

📘

Ensemble Methods: Stacking, Blending & Voting Topic

Hard vs soft voting, stacking with meta-learners, blending strategies used in Kaggle competitions

55 min

📘

Model Interpretability: SHAP & LIME Topic

Global vs local explanations, SHAP values, force plots, summary plots, LIME for black-box models

55 min

💻

Exercise: Model Debugging Session Exercise

Given an underperforming model, diagnose issues using learning curves, SHAP, and error analysis

1h 15m

🔬

Lab 5: Optuna Hyperparameter Search Lab 🧪 Hands-on Lab · Intermediate

Use Optuna to tune an XGBoost model; visualize the optimization history and parameter importance

1h 30m

📋

Evaluation & Optimization Assessment Quiz

Scenario-based questions on CV strategy selection, HPO trade-offs, and ensemble decision-making

30 min

MOD 07

ML Pipelines & Experiment Tracking (MLOps Foundations)

Adopt MLOps practices: experiment tracking, data versioning, and reproducibility.

8h 40m ▾

📘

Introduction to MLOps: Principles & Maturity Levels Topic

Why ML projects fail in production, the MLOps lifecycle, Google's MLOps maturity model

45 min

📘

MLflow: Experiment Tracking & Model Registry Topic

mlflow.log_param/metric/artifact, autologging, model registry, model stages, MLflow UI

1 hr

📘

Data Versioning with DVC Topic

DVC init, add, push, pull, pipeline stages (dvc.yaml), remote storage (S3/GCS), reproducing experiments

55 min

📘

Reproducibility & Configuration Management Topic

Hydra/YAML configs, seed management, environment pinning, Makefile workflows for ML projects

50 min

📖

Reading: ML Technical Debt Paper Reading

Summary of Google's seminal "Hidden Technical Debt in ML Systems" paper with discussion prompts

40 min

🔬

Lab 6: Track Your Experiments with MLflow Lab 🧪 Hands-on Lab · Intermediate

Run 5 experiments, compare in MLflow UI, register the best model, and promote it to staging

1h 30m

🛠️

Mini Project 3: Reproducible ML Experiment Suite Mini Project 🧪 Mini Project · Intermediate

Full pipeline: DVC-tracked data + MLflow-tracked experiments + Hydra configs; must be reproducible by reviewer

3 hrs

MOD 08

Model Deployment — From Notebook to Production API

Ship models to production: serve them as APIs, containerize, and monitor in the wild.

10h 5m ▾

📘

Serving ML Models: Deployment Patterns Topic

Batch vs real-time inference, REST vs gRPC, online vs offline serving, serverless ML

50 min

📘

Building a Model API with FastAPI Topic

FastAPI routing, Pydantic schemas, request validation, async endpoints, OpenAPI docs, loading joblib models

1h 5m

📘

Containerization with Docker Topic

Docker concepts, writing Dockerfiles for ML APIs, docker-compose for multi-service apps, image optimization

1 hr

📘

Model Monitoring & Data Drift Detection Topic

Evidently AI for drift monitoring, concept drift vs data drift, alerting strategies, retraining triggers

50 min

🔬

Lab 7: Deploy Your First ML API Lab 🧪 Hands-on Lab · Intermediate

Wrap a trained model in FastAPI, add prediction endpoints, validate inputs, test with Postman/curl

1h 40m

🔬

Lab 8: Dockerize & Deploy Your ML Service Lab 🧪 Hands-on Lab · Intermediate

Write a multi-stage Dockerfile, build and run the container, push to Docker Hub, deploy to Render/Railway

1h 40m

🛠️

Mini Project 4: End-to-End Deployed ML Service Mini Project 🧪 Mini Project · Intermediate

Train a model, expose as FastAPI, containerize with Docker, deploy to cloud, add monitoring — live URL submission

3 hrs

MOD 09

Capstone — Full ML Engineering Pipeline

Bring it all together: a multi-phase capstone covering data, modeling, deployment, and review.

15h 45m ▾

📘

Capstone Kickoff: Brief, Domains & Evaluation Rubric Topic

Project domains: healthcare prediction, retail forecasting, NLP classification, financial risk, social impact

45 min

💬

Capstone Peer Review & Panel Showcase Forum

Review 3 projects with structured rubric; top projects enter the Course Showcase Gallery

1h 30m

🏆

Phase 1: Problem Definition & Data Engineering Capstone 🧪 Capstone Project · Intermediate

Problem framing, data sourcing, full cleaning + feature engineering pipeline, DVC versioning

3h 30m

🏆

Phase 2: Model Development & Optimization Capstone 🧪 Capstone Project · Intermediate

Train multiple models, hyperparameter tuning with Optuna, MLflow experiment tracking, final model selection

3h 30m

🏆

Phase 3: Deployment & Monitoring Capstone 🧪 Capstone Project · Intermediate

FastAPI service, Docker, cloud deployment, drift monitoring setup, API documentation

3h 30m

🏆

Phase 4: Technical Report & Video Demo Capstone 🧪 Capstone Project · Intermediate

Write ML design document, record 8-min system walkthrough, submit GitHub repo + live URL

3 hrs

MOD 10

Industry Readiness & Career Acceleration

Prepare for ML engineering interviews and craft a portfolio hiring managers notice.

3 hrs ▾

🧭

ML Engineer Interview Deep Dive Career

Common interview types (ML design, coding, ML theory), company-specific formats (FAANG, startups), preparation strategy

50 min

💻

Exercise: Mock ML System Design Interview Exercise

Design a recommendation system or fraud detection system in 45 minutes; self-assess against rubric

1 hr

📖

Reading: Building Your ML Portfolio for Maximum Impact Reading

What hiring managers look for, GitHub repo best practices, writing ML project READMEs, Kaggle rank relevance

30 min

🎯

Activity: Craft Your ML Engineer Story Activity

Write your professional summary, identify 3 portfolio projects to polish, and map your target companies

40 min

Machine Learning Engineering: Build, Optimize & Deploy Intelligent Models

Machine Learning Engineering: Build, Optimize & Deploy Intelligent Models — June Cohort 2026

Machine Learning Engineering: Build, Optimize & Deploy Intelligent Models — July Cohort 2026

$2,500

Frequently asked questions about this program

Labs used in this course

Jupyter Notebook Lab

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