Six guided pathways from fundamentals to capstone

Our curriculum is designed for Canadian learners upskilling in artificial intelligence — developers, data analysts, and career changers who want structured neural network training rather than scattered tutorials. Each programme maps to vocational training standards with PyTorch labs, model training exercises, and completion certificates that attest to course participation. Programme codes follow our internal pathway navigator: NP-001 through NP-009 cover the core deep learning engineering stack taught at our Hamilton Hunter Street campus and through live online cohort delivery nationwide.

NP-001

Neural Network Fundamentals

12 weeks · C$1,790 – C$2,140

Your entry waypoint into artificial neural networks. This programme builds intuition for perceptrons, multilayer perceptrons, activation functions, loss functions, backpropagation, and gradient descent from first principles. You will implement forward passes, tune learning rates, and complete introductory model training labs in Python. Prerequisites include comfortable Python fundamentals and basic linear algebra — we state these clearly before enrolment and do not assume prior deep learning experience. Weekly cohort sessions include whiteboard walkthroughs, pair debugging, and office hours with instructors who specialise in vocational AI training rather than consulting deliverables.

NP-003

CNNs & Computer Vision

10 weeks · C$2,050 – C$2,490

Progress from dense layers to convolutional neural networks engineered for image classification, object detection foundations, and computer vision portfolio projects. Modules cover kernels, pooling, batch normalisation, transfer learning, and data augmentation pipelines. You will train CNN architectures in PyTorch, evaluate model performance on held-out sets, and document results for your professional portfolio. Recommended after NP-001 or equivalent experience with backpropagation and tensor operations. Hybrid Hamilton lab intensives are available for learners who want in-person pathway advisory alongside live online lectures.

NP-004

RNNs & Sequence Modelling

10 weeks · C$2,150 – C$2,590

Explore recurrent neural networks, LSTM and GRU cells, sequence-to-sequence modelling, and introductory natural language processing workflows. This pathway teaches how temporal dependencies are encoded in neural architectures, how vanishing gradients are mitigated, and how sequence models are evaluated against baselines. Labs include text classification, sentiment analysis, and time-series forecasting exercises with honest discussion of when simpler models outperform deep recurrent stacks. Completion prepares you for NP-005 transformer fundamentals with a solid grounding in sequential data representation.

NP-005

Transformers & Attention

10 weeks · C$2,290 – C$2,750

Study self-attention mechanisms, positional encodings, encoder-decoder architectures, and the transformer fundamentals that underpin modern large language models and generative AI systems. You will implement scaled dot-product attention, build transformer blocks in PyTorch, and explore prompt engineering contexts with appropriate scepticism toward hype. Modules distinguish computational neural models from neuroscience therapy or brain wellness programmes — when we say neural, we mean machine learning engineering. This programme is ideal for learners who have completed NP-003 or NP-004 and want to understand attention-based architectures used in production NLP pipelines.

NP-007

PyTorch Engineering Lab

8 weeks · C$1,990 – C$2,380

A practical engineering waypoint focused on PyTorch as your primary deep learning framework. Topics include tensor operations, custom datasets, DataLoader patterns, GPU utilisation, checkpointing, mixed-precision training, and reproducible experiment tracking. You will refactor notebook prototypes into maintainable training scripts, implement custom loss functions and optimisers, and apply regularisation techniques that transfer directly to workplace ML engineering. This programme can be taken alongside architecture-focused pathways or as a skills bridge for developers migrating from TensorFlow modules to PyTorch-first workflows.

NP-009

AI Certification Capstone

8 weeks · C$2,390 – C$2,850

The culminating waypoint of the NeuralPathway route map. You will scope an end-to-end capstone project — from problem definition through model training, model evaluation, error analysis, and professional presentation. Instructors provide milestone reviews, presentation coaching, and feedback on documentation quality. Upon successful completion of all module milestones and capstone deliverables, you receive a completion certificate reflecting course participation and training outcomes. This certificate does not constitute a university degree, professional licensure, or guaranteed employment — outcomes depend on your prerequisites, practice time, and individual aptitude.

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NeuralPathway provides vocational training in artificial neural networks and deep learning. Completion certificates reflect course participation — not university degrees, professional licensure, or guaranteed employment. Learning outcomes depend on prerequisites met, practice time, and individual aptitude. When we reference neural, we mean computational models in machine learning — not neuroscience therapy, brain training, or mental health services. Course exercises may use AI-assisted tools; outputs require human verification. We are not an AI consulting agency, web studio, or IT outsourcing firm.