Diploma in Applied Machine Learning
End-to-end ML engineering: classical algorithms, deep learning with PyTorch, transformers, and production deployment.
Diploma in Applied Machine Learning is a self-paced online intermediate diploma in machine-learning from Altaris AI Academy. Tuition is $399 CAD (payable in three interest-free instalments), most students finish in about 6 months at 11 hours a week, and graduates receive a 18-credit, employer-verifiable diploma. 30-day money-back guarantee.
About this program
A comprehensive 6-month diploma program covering modern machine learning engineering from mathematical foundations through cloud deployment. Master feature stores, transformer architectures, MLOps evaluation harnesses, and latency optimization.
Who this program is for
- →Working professionals moving into machine-learning roles
- →Analysts, developers, and product people leveling up on applied AI
- →Canadian residents seeking a verifiable diploma credential
Topics you'll cover
6 modules across 6 months — 29 lessons in total.
Full syllabus
Module 1 · Module 1: Foundations of Applied Statistical Learning▾
- L1Mathematical review: gradients, tensors, and probability distributions
- L2Advanced feature engineering and automated scaling pipelines
- L3Tree-based ensembles (XGBoost, LightGBM, CatBoost) in production
- L4Dimensionality reduction, clustering, and anomaly detection algorithms
- L5Module 1 Capstone: Tabular fraud detection pipeline with class imbalance tuning
Module 2 · Module 2: Deep Learning Architectures & PyTorch▾
- L1Custom autograd mechanics, loss functions, and optimizers in PyTorch
- L2Convolutional architectures, transfer learning, and attention mechanisms
- L3Sequence modelling, LSTMs, and early transformer precursors
- L4Hyperparameter optimization using Ray Tune and Optuna
- L5Module 2 Capstone: Multimodal classification service with PyTorch Lightning
Module 3 · Module 3: Modern Transformer Systems & Fine-Tuning▾
- L1Self-attention mathematics, positional encodings, and FlashAttention
- L2BERT, RoBERTa, and encoder embeddings for semantic search
- L3LoRA, QLoRA, and parameter-efficient fine-tuning (PEFT)
- L4Quantization methods: GGUF, AWQ, and INT4/INT8 precision tradeoffs
- L5Module 3 Capstone: Domain-adapted clinical NLP pipeline with custom tokenizers
Module 4 · Module 4: MLOps, CI/CD & Model Observability▾
- L1Feature store architecture with Feast and Redis
- L2CI/CD pipelines for ML models with GitHub Actions and CML
- L3Data drift, concept drift, and performance monitoring with Evidently AI
- L4Model registries, versioning, and rollback strategies with MLflow
- L5Module 4 Capstone: Automated continuous training loop with drift triggers
Module 5 · Module 5: Scalable Inference & Cloud Serving▾
- L1Packaging models with ONNX Runtime and TensorRT
- L2Asynchronous batching and concurrency in FastAPI and Triton Inference Server
- L3Serverless GPU architectures and cost-effective cloud scaling
- L4Security hardening, model red-teaming, and payload sanitization
- L5Module 5 Capstone: Sub-50ms high-throughput inference microservice
Module 6 · Module 6: Capstone Practicum & Industry Defense▾
- L1Project scoping, architectural review, and technical spec defense
- L2Full system implementation, benchmarking, and documentation
- L3Code review, load testing, and security audit
- L4Final live technical defense and portfolio verification
What you'll be able to do
- ●Architect, train, and validate deep neural networks using PyTorch
- ●Implement production feature pipelines and automated data drift monitors
- ●Fine-tune pre-trained vision and language models for domain-specific tasks
- ●Deploy low-latency inference endpoints with Docker, FastAPI, and Triton
- ●Establish rigorous regression testing suites and continuous model evaluation
- ●Build PIPEDA-compliant model governance and auditable telemetry pipelines
Career paths after graduation
Frequently asked questions
How much does the Diploma in Applied Machine Learning cost?▾
Tuition is $399 CAD, paid once by invoice: apply online, we review your application and email an invoice, and your program opens once it is paid. A 30-day refund window applies from your enrollment date.
How long is the Diploma in Applied Machine Learning program?▾
Self-paced. Most students complete it in 6 months at roughly 7 hours per week, but you can go faster or slower — you keep lifetime access.
What are the prerequisites?▾
Proficiency in Python programming (loops, functions, object-oriented concepts); Basic understanding of linear algebra and calculus (matrix multiplication, derivatives); Familiarity with data manipulation using Pandas or NumPy
What credential does the program award?▾
Graduates receive the Altaris AI Academy Diploma in machine-learning — a verifiable credential with a unique certificate number you can publish on LinkedIn and that any employer can verify at altarisai.org/verify. Employer acceptance is determined by each employer; it is not a regulated professional designation.
What is the refund policy?▾
Full refund within 30 days of enrollment, no questions asked. After day 30, prorated refunds are available per our Refund Policy.
Who teaches the program?▾
Working Canadian AI practitioners — not academics. Every module is built and reviewed by a lead instructor working in the field today.