AI & Machine Learning Engineering Program

The AI & Machine Learning Engineering Program is a 6-month course from ALA Academy, followed by a 6-month embedded internship with ALA Core on real AI/ML projects. You will learn to frame ML problems, build and evaluate models with scikit-learn and PyTorch, serve them through APIs, and deploy and monitor them responsibly.

Course Structure

- Duration: 6 months / 24 weeks

- Sessions: 72 instructor-led sessions (3 per week, 2 hours each)

- Total hours: 144 instructional hours

- Months 1-5: 60 AI/ML engineering sessions

- Month 6: 12 career-readiness sessions (CV, LinkedIn, GitHub portfolio, interviews, graduation presentation)

- Internship: 26 weeks with ALA Core

- Entry level: beginner-friendly, with basic computer literacy required

What You Will Learn

- Python, NumPy, pandas, Git, testing, and reproducible project structure

- Mathematics for ML: statistics, probability, linear algebra, and optimization intuition

- The ML workflow: baselines, preprocessing pipelines, and preventing data leakage

- Regression, classification, and evaluation metrics (precision, recall, F1, ROC-AUC)

- Tree models, ensembles, model selection, and feature engineering

- Interpretability, error analysis, and fairness checks

- Unsupervised learning, anomaly detection, and recommendation foundations

- Classical NLP and time-aware ML

- Deep learning with PyTorch: training loops, CNNs, and transfer learning

- Transformers, pretrained models, generative AI, and Retrieval-Augmented Generation (RAG)

- Experiment tracking with MLflow

- Model serving with FastAPI and containers with Docker

- Testing, CI with GitHub Actions, deployment, and monitoring

- Responsible AI: privacy, security, model cards, and human oversight

Projects and Assessment

- Project 1: Supervised tabular ML solution

- Project 2: Classical ML training pipeline and prediction API

- Project 3: Deep learning, NLP, computer vision, or grounded generative AI application

- An integrated capstone project (25% of the grade)

- Career package and graduation presentation before an ALA Core panel

Career Month

Build a technical CV, a LinkedIn profile, and a GitHub portfolio with model cards and reproducible demos. Complete a technical mock interview and present your graduation project.

Real-World Internship

Work alongside ALA Core ML, data, software, QA, and product teams on live internal or client AI/ML projects with approved datasets and real repositories. You get least-privilege access under an assigned mentor and do not approve models or deploy to production independently.

Who Is This For?

Beginners and career changers who want to become AI or machine learning engineers.