top of page

Course Series

Explore our course series designed to help you build foundational and intermediate knowledge across key topics. Each course offers curated content tailored to different learning goals.

INTRODUCTORY COURSES

4 courses

  • Why take this course?

    This course introduces learners to Agentic AI - systems that go beyond passive response generation and instead act autonomously toward goals. In contrast to traditional AI tools, agentic systems can plan, make decisions, use external tools, and even reflect on their actions. Designed for learners who have already explored Generative AI, this course bridges the gap between using AI and designing AI-driven workflows that simulate autonomous behavior.
    Through hands-on projects and real-world examples, students will gain foundational knowledge in multi-step reasoning, task decomposition, tool integration, and memory - the building blocks of agentic systems. This course sets the stage for future industry-specific tracks in business, education, finance, and more.

    AI is evolving — and it's learning to take initiative. This course explores the next frontier: Agentic AI. Unlike basic chatbots or assistants, agentic systems can plan, act, and adapt to achieve goals. You’ll learn how these intelligent agents work, how to design them, and how they use tools, memory, and multi-step reasoning to function independently. Perfect for professionals, technologists, or innovators who want to go beyond using AI and start building with it. This course lays the foundation for advanced, industry-specific agentic AI training.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: What is Agentic AI?

      • Definitions: Agents vs Assistants vs Tools

      • Agent architecture: Perception → Reasoning → Action

      • Task completion vs goal pursuit

      • Key frameworks: Auto-GPT, BabyAGI, LangChain agents, CrewAI

      • Demos: How agents differ from GenAI tools

      Session 2: Planning and Reasoning in Agents

      • Multi-step reasoning: from LLMs to planning agents

      • Task decomposition and sub-goal generation

      • Prompt-chaining vs autonomous reasoning

      • Case study: An AI that plans a marketing campaign or writes code

    • Session 3: Tool Use and Integration

      • Toolformer and beyond: how agents call APIs, search, use calculators

      • The agent “toolbelt”: defining and assigning tools

      • LLM + plugin architectures (LangChain, OpenAI functions, HuggingFace agents)

      • Hands-on: Design a simple agent to perform web search + summarize

      Session 4: Memory and Context

      • Short-term vs long-term memory in agents

      • Retrieval-augmented generation (RAG) and vector databases

      • Embeddings 101 and memory stores (Pinecone, FAISS)

      • Use case: AI assistant with persistent memory of a client or task

    • Session 5: Reflection and Self-Correction

      • Agentic loops and thinking steps (e.g., ReAct framework)

      • Iterative reasoning, retry logic, and error handling

      • Critic and planner roles in multi-agent systems

      • Use case: Agents that debug or revise their own outputs

      Session 6: Multi-Agent Collaboration

      • Autonomous teams: CrewAI, AutoGen, LangGraph

      • Roles and personas in agentic systems

      • Inter-agent communication

      • Use case: Agent team executes a content pipeline or workflow

    • Session 7: Limitations, Ethics, and Failure Modes

      • Over-reliance and hallucination in agentic behavior

      • Security, misuse, and safety concerns

      • Prompt injections, tool misuse, data privacy

      • Design principles for safe, bounded autonomy

      • What current agentic systems can’t do (yet)

      Session 8: Capstone Workshop — Design Your Own Agent

      • Choose a goal or workflow (e.g., research assistant, business analyst, personal concierge)

      • Define tools, memory, goals, roles

      • Create a prompt-based or low-code agent prototype

      • Demo and feedback: peer review + instructor insights

  • Why take this course?

    This hands-on, beginner-friendly course introduces learners to the fast-evolving field of prompt engineering - the art and science of communicating effectively with Generative AI. In just 24 hours, participants will learn how to craft prompts that produce better, more accurate, and more useful outputs from tools like ChatGPT, Claude, DALL-E, and others. Through real-world examples, interactive exercises, and guided experimentation, learners will gain practical skills to unlock the full potential of AI in creative, professional, and technical contexts.


    No technical or programming background is required - just curiosity and a willingness to experiment!

    Generative AI is powerful—if you know how to ask the right questions. This course teaches you how to speak the language of AI through effective prompt engineering. Over four weeks, you'll learn how to design, refine, and experiment with prompts to unlock smarter, more accurate, and more creative responses from GenAI tools like ChatGPT, Claude, and Midjourney. From writing and coding to design and customer service, prompt engineering is the skill that puts AI to work for you. Perfect for professionals, creators, educators, and anyone curious about working better with AI.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: What is Generative AI? A Beginner’s Map

      • What is GenAI? Definitions and context

      • Evolution: From early AI to Generative AI

      • Examples in action: ChatGPT, DALL·E, Midjourney, GitHub Copilot, etc.

      • Real-world applications (business, education, art, software, etc.)

      • What GenAI can do today

      Session 2: How Generative AI Works — The Basics (No Math!)

      • What is a model? What is "training"?

      • Overview of LLMs, diffusion models, GANs, VAEs

      • Why GenAI seems smart (but isn’t conscious)

      • Tokens, prompts, hallucinations explained

      • Limitations of current models and where they break

    • Session 3: Exploring Text-Based GenAI (LLMs like ChatGPT, Claude, Gemini)

      • How LLMs generate language

      • Prompting basics: getting what you want

      • Use cases: summarization, writing, planning, coding

      • Prompt engineering basics and best practices

      • Limitations and risks of text-based GenAI

      Session 4: GenAI for Images, Audio, and Video

      • Overview of image generation tools (DALL·E, Midjourney, Stable Diffusion)

      • Basics of how image GenAI works (diffusion explained simply)

      • Intro to audio and music GenAI (e.g., Suno, ElevenLabs)

      • Video GenAI (e.g., Runway, Sora—emerging tools)

      • Demos and ethical considerations (deepfakes, misuse, bias)

    • Session 5: Real-World Use Cases and Tools

      • Business use: marketing, productivity, customer service

      • Education use: tutoring, writing support, personalized learning

      • Design, art, and creative content

      • GenAI in software development and automation

      • Demo of multi-modal GenAI tools (text+image, text+code)

      Session 6: What GenAI Can’t Do (Yet)

      • Understanding hallucination and misinformation

      • Reasoning, logic, and factual gaps

      • Biases and fairness in GenAI

      • Creativity vs replication: is GenAI truly creative?

      • The future: AGI vs narrow GenAI

    • Session 7: Ethics, Risks, and Responsible Use

      • Copyright and IP issues

      • Deepfakes, misinformation, and manipulation

      • AI safety and alignment challenges

      • Open vs closed-source models

      • Regulatory trends (Canada, EU, US, etc.)

      Session 8: Hands-On Workshop: Build Your Own GenAI Workflow

      • Choose a use case (content creation, customer response, design, etc.)

      • Hands-on with GenAI tools (text, image, audio)

      • Crafting effective prompts and refining results

      • Wrapping up: GenAI literacy for everyday use

      • Final Q&A and resources for continued learning

  • Why take this course?

    This 24-hour introductory course is designed for absolute beginners who want to understand Generative AI (GenAI) and how it is transforming industries. Through 8 engaging sessions, participants will explore how GenAI works, what it's good at (and what it isn't), and how to begin using GenAI tools in real-world scenarios. You’ll gain foundational knowledge of the technologies behind GenAI, including large language models (LLMs), image generation, and audio synthesis. We'll also discuss the risks,
    ethical concerns, and current limitations to using GenAI responsibly. By the end of the course, learners will be confident in using popular GenAI tools, understanding how they work under the hood, and recognizing their potential and boundaries in various applications.


    No prior technical knowledge is required.

    Unlock the power of Generative AI in just four weeks. This hands-on, beginner-friendly course will take you from zero to confident user by exploring how GenAI tools like ChatGPT, DALL·E, and others work—and how they’re being used across industries. Learn what GenAI can (and can’t) do, experiment with leading tools, and understand the ethical, legal, and societal implications of this rapidly evolving technology. Whether you're a business professional, educator, artist, or curious learner, this course offers practical skills and critical insights to help you navigate and apply GenAI effectively.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: What is Generative AI? A Beginner’s Map

      • What is GenAI? Definitions and context

      • Evolution: From early AI to Generative AI

      • Examples in action: ChatGPT, DALL·E, Midjourney, GitHub Copilot, etc.

      • Real-world applications (business, education, art, software, etc.)

      • What GenAI can do today

      Session 2: How Generative AI Works — The Basics (No Math!)

      • What is a model? What is "training"?

      • Overview of LLMs, diffusion models, GANs, VAEs

      • Why GenAI seems smart (but isn’t conscious)

      • Tokens, prompts, hallucinations explained

      • Limitations of current models and where they break

    • Session 3: Exploring Text-Based GenAI (LLMs like ChatGPT, Claude, Gemini)

      • How LLMs generate language

      • Prompting basics: getting what you want

      • Use cases: summarization, writing, planning, coding

      • Prompt engineering basics and best practices

      • Limitations and risks of text-based GenAI

      Session 4: GenAI for Images, Audio, and Video

      • Overview of image generation tools (DALL·E, Midjourney, Stable Diffusion)

      • Basics of how image GenAI works (diffusion explained simply)

      • Intro to audio and music GenAI (e.g., Suno, ElevenLabs)

      • Video GenAI (e.g., Runway, Sora—emerging tools)

      • Demos and ethical considerations (deepfakes, misuse, bias)

    • Session 5: Real-World Use Cases and Tools

      • Business use: marketing, productivity, customer service

      • Education use: tutoring, writing support, personalized learning

      • Design, art, and creative content

      • GenAI in software development and automation

      • Demo of multi-modal GenAI tools (text+image, text+code)

      Session 6: What GenAI Can’t Do (Yet)

      • Understanding hallucination and misinformation

      • Reasoning, logic, and factual gaps

      • Biases and fairness in GenAI

      • Creativity vs replication: is GenAI truly creative?

      • The future: AGI vs narrow GenAI

    • Session 7: Ethics, Risks, and Responsible Use

      • Copyright and IP issues

      • Deepfakes, misinformation, and manipulation

      • AI safety and alignment challenges

      • Open vs closed-source models

      • Regulatory trends (Canada, EU, US, etc.)

      Session 8: Hands-On Workshop: Build Your Own GenAI Workflow

      • Choose a use case (content creation, customer response, design, etc.)

      • Hands-on with GenAI tools (text, image, audio)

      • Crafting effective prompts and refining results

      • Wrapping up: GenAI literacy for everyday use

      • Final Q&A and resources for continued learning

  • Why take this course?

    This 24-hour introductory course offers a hands-on, non-technical introduction to the most impactful AI tools available today. Designed for curious beginners from any background, the course showcases real-world applications of AI in writing, image creation, productivity, communication, data analysis, and more. Through guided demos and hands-on practice, learners will discover how to use AI tools to boost creativity, improve efficiency, and solve everyday problems - no programming required. By the end of the course, participants will understand the capabilities, limitations, and best uses of today’s most promising AI tools, and feel empowered to start using them in their personal and professional lives.

    AI is no longer just for coders and data scientists—it’s for everyone. This beginner-friendly course introduces you to today’s most powerful and accessible AI tools, from ChatGPT to Canva AI, and shows you how to use them to write faster, design smarter, automate tasks, create content, and more. Learn by doing through real-world demos and hands-on challenges as you build your own custom AI toolkit. Whether you're a professional, student, entrepreneur, or just curious, this course will show you what’s possible when AI works with you.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: Welcome to the AI Tool Ecosystem

      • What are AI tools and why are they everywhere now?

      • Categories of AI tools: text, images, audio, video, productivity, etc.

      • Core concepts: Generative AI, automation, personalization

      • Intro to foundational tools: ChatGPT, DALL·E, Grammarly, Notion AI

      • Hands-on: Try your first AI-assisted task

      Session 2: Text-Based Tools and Assistants

      • ChatGPT, Claude, Gemini: what they can do and how they differ

      • Writing support: GrammarlyGO, Jasper, Notion AI

      • Email, reports, summarization, translation, and rewriting

      • Hands-on: Crafting prompts and workflows for writing tasks 

    • Session 3: Visual AI: From Images to Design

      • Image generators: DALL·E, Canva AI, Adobe Firefly

      • Design help: Layout suggestions, branding ideas, social media content

      • Practical uses: marketing, personal branding, storytelling

      • Hands-on: Create custom visuals with AI

      Session 4: Audio, Video, and Voice AI Tools

      • Voice AI: ElevenLabs, Descript

      • Music generation: Suno AI, Soundraw

      • Video tools: Synthesia, Runway, Pictory

      • Use cases: explainer videos, training, podcasts, voiceovers

      • Hands-on: Generate a short video or podcast clip

    • Session 5: AI Tools for Productivity & Automation

      • Notion AI, Microsoft Copilot, Google Duet, Superhuman

      • Meeting transcription: Otter.ai, Fireflies

      • Scheduling, brainstorming, note-taking, content planning

      • Hands-on: Build a personal productivity stack with AI

      Session 6: AI for Data and Research

      • Tools: ChatGPT (with data plugins), Perplexity AI, Browse AI

      • AI search and research assistants

      • Visualizing data with AI: ChatGPT + charts, Power BI integrations

      • Hands-on: Use AI to analyze or summarize a dataset or research topic

    • Session 7: Responsible Use and Tool Comparison

      • AI hallucination and fact-checking

      • Privacy, data ownership, copyright

      • Free vs paid tools: what’s worth it?

      • Tool longevity and choosing sustainable platforms

      • Hands-on: Compare tool results for the same task

      Session 8: Build Your Own AI Toolkit (Capstone Workshop)

      • Pick your domain: business, education, content creation, etc.

      • Identify useful tools for your goals

      • Build a repeatable AI-powered workflow

      • Final showcase: present your personalized AI toolkit

APPLIED
COURSES

8 courses

  • Why take this course?

    This course offers a foundational overview of how artificial intelligence (AI) tools are reshaping health care and social services. Learners will explore AI applications in diagnostics, treatment planning, documentation, workflow automation, and patient interaction. Emphasis is placed on real-world tools currently being deployed across medical and social work settings, including predictive analytics, natural language processing, and AI-assisted mental health support. By the end of the course, learners will understand the transformative potential of AI in delivering more efficient, personalized, and equitable care.

    Discover how AI is revolutionizing health care and social services! This introductory course explores cutting-edge tools reshaping diagnostics, treatment, and support systems. Perfect for professionals and learners aiming to stay ahead in a rapidly evolving field, you’ll learn how to navigate the latest technologies in clinical prediction, documentation, patient interaction, and resource management. Transform your understanding of modern care—no technical background required.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: 

      • Overview of AI in health care and social services

      • Key trends and emerging use cases

      • How AI tools integrate with care delivery systems

      • Challenges and limitations of current tools

      • Real-world success stories from clinics and agencies

      Session 2: 

      • Categories of AI tools: predictive, generative, assistive

      • Understanding structured vs. unstructured data in health settings

      • Introduction to no-code/low-code AI tools for clinicians

      • Tool showcase: Microsoft Cloud for Health, Google Health AI

      • Demo: Using a free AI chatbot for triage or intake support

    • Session 3: 

      • AI in diagnostics and clinical decision-making

      • Case studies: oncology, cardiology, and primary care

      • Tool showcase: PathAI, IBM Watson Health

      • Demo: Using symptom checker tools (e.g., Ada Health, Babylon)

      • Ethical implications of machine-based diagnosis

      Session 4: 

      • Natural language processing (NLP) in electronic medical records (EMRs)

      • Speech-to-text for physician notes and documentation

      • Tool showcase: Nuance DAX, Amazon Comprehend Medical

      • Patient chatbots and conversational AI tools in support settings

      • Demo: Using NLP for generating summaries or care notes

    • Session 5:

      • Workflow automation in administrative and care settings

      • AI schedulers, billing support, and referral routing

      • Tool showcase: Olive AI, Notable Health

      • Care coordination and remote patient monitoring tools

      • Demo: Automating appointment scheduling with AI assistants

      Session 6: 

      • AI in mental health and social services

      • Tools for virtual screening and behavioral monitoring

      • Tool showcase: Woebot, Wysa, and other digital mental health platforms

      • AI for crisis line analysis and suicide prevention

      • Ethical guardrails and privacy in mental health data

    • Session 7:

      • Wearables, sensors, and real-time data monitoring

      • Applications in chronic care, elder care, and fitness tracking

      • Tool showcase: Apple HealthKit, Fitbit, Biofourmis

      • Remote therapeutic monitoring (RTM) and AI alerts

      • Using data from smart devices in care planning

      Session 8: 

      ​

      • Tool selection frameworks for your organization

      • Regulatory and legal considerations in using AI tools

      • Hands-on review of a health-focused AI platform (group activity)

      • Final reflections: Responsible and equitable AI use in care

      • Planning next steps: Further learning and specialization options

      • Choose a scenario: automate a process, build an app, or streamline a workflow

      • Use multiple tools from the course to build your prototype

      • Peer review and improvement session

  • Why take this course?

    This course provides a deep dive into how predictive analytics is transforming clinical decision-making. Students will learn to evaluate, interpret, and apply AI models that forecast patient outcomes, stratify risk, and personalize treatment plans. Real-world tools and scenarios will be used to demonstrate the power of AI in diagnostics, early intervention, and care optimization. Designed for healthcare professionals, analysts, and policy developers, this course emphasizes responsible integration of AI into clinical workflows.

    Harness the power of data to make smarter decisions in healthcare. This course equips participants with an understanding of AI-powered predictive tools that improve diagnostics, anticipate risk, and enhance care delivery. Ideal for clinicians, healthcare administrators, and digital health strategists aiming to implement predictive insights into real-world practice.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1:  Foundations of Predictive Analytics in Health Care

      • Key concepts: supervised learning, classification, regression

      • Overview of data sources (EMRs, lab results, imaging, genomics)

      • Predictive modeling vs traditional statistics

      • Use cases in preventive and personalized medicine

      • Introduction to clinical prediction models (e.g., APACHE, SOFA)

      Session 2: Data Preparation and Model Selection

      • Data cleaning, normalization, and handling missing data

      • Feature selection and dimensionality reduction

      • Common predictive models (logistic regression, decision trees, random forests)

      • Bias, overfitting, and underfitting in healthcare datasets

      • Evaluation metrics (ROC, AUC, sensitivity/specificity)

    • Session 3: Predictive Risk Stratification and Triage

      • Tools for readmission prediction, deterioration alerts, sepsis detection

      • Early warning systems in inpatient care (e.g., NEWS2, MEWS)

      • AI in emergency room triage and prioritization

      • Cost-benefit considerations in risk model deployment

      • Case studies from hospital systems

      Session 4: Personalized Treatment Planning

      • Tools for readmission prediction, deterioration alerts, sepsis detection

      • Early warning systems in inpatient care (e.g., NEWS2, MEWS)

      • AI in emergency room triage and prioritization

      • Cost-benefit considerations in risk model deployment

      • Case studies from hospital systems

    • Session 5: Real-Time Predictive Analytics and Streaming Data

      • Using real-time monitoring from wearables and bedside devices

      • Stream processing tools (Apache Kafka, Spark Streaming)

      • Building alerts and triggers based on thresholds or trend deviations

      • Latency, reliability, and data quality challenges

      • Examples from ICUs and chronic disease management

      Session 6: Deploying Predictive Models in Clinical Workflows

      • EMR integration strategies (FHIR, HL7 standards)

      • User interface considerations and clinician trust

      • Evaluating real-world performance (prospective validation)

      • Change management and staff training

      • Avoiding alarm fatigue and automation bias

    • Session 7: Tool Demonstrations and Use-Case Simulations

      • Hands-on with predictive dashboards and platforms (e.g., Epic Cogito, Jvion, KenSci)

      • Exploring open-source notebooks and sandbox environments

      • Working through a stroke prediction model or diabetic readmission model

      • Critically evaluating strengths and gaps of different tools

      • Student discussion: tool adoption in their settings

      Session 8: Trends, Ethics, and Future of Predictive Analytics

      • Federated learning and privacy-preserving analytics

      • Regulatory landscape and medical AI approvals

      • Equity in AI risk prediction across populations

      • Next-gen use cases: digital twins, early screening from social data

      • Course recap and participant roadmap for implementation

  • Why take this course?

    This course introduces participants to the use of AI-powered tools and technologies that enhance efficiency and effectiveness in health care workflows. Learners will explore automation solutions designed to reduce administrative burden, improve appointment scheduling, optimize hospital resources, and facilitate coordinated care. Through practical demos and case studies, students will evaluate real-world implementations of automation in various health contexts, from small clinics to large hospital networks.

    Discover how AI can streamline and transform health care delivery! In this hands-on course, you’ll explore cutting-edge automation tools that support resource planning, staff scheduling, patient triage, and care coordination. Learn how to improve health care outcomes and reduce burnout by harnessing the power of AI-driven workflows.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: 

      •  Introduction to Workflow Challenges in Health Care

      • • Overview of AI in Operations Management

      • • Case Studies: Inefficiencies in Manual Processes

      • • Key Metrics: Throughput, Wait Times, Utilization

      Session 2: 

      • AI for Appointment Scheduling and Patient Flow

      • Predictive Tools for Reducing Wait Times

      • Queue Management Algorithms in Clinics

      • Integration with EHR Systems

    • Session 3: 

      • Resource Allocation in Hospitals Using AI

      • Staffing Optimization Models

      • Real-time Bed Management Tools

      • Case Study: Emergency Department Resource Planning

      Session 4: 

      • Automation of Administrative Tasks

      • AI for Claims Processing and Billing

      • Chatbots for Routine Inquiries

      • Reducing Paperwork for Clinicians

    • Session 5: 

      • Care Coordination and Interdisciplinary Communication

      • Automated Alerts and Task Routing

      • AI in Patient Handoff and Transition Planning

      • Integrated Dashboards for Team Collaboration

      Session 6: 

      • Remote Monitoring and Automated Interventions

      • Wearable Integration and Alert Systems

      • AI-driven Patient Risk Stratification

      • Automation in Home and Community Care

    • Session 7: 

      • Evaluating Automation Tools for ROI and Usability

      • Key Performance Indicators and Benchmarks

      • Implementation Challenges and Change Management

      • Vendor Selection and Procurement Considerations

      Session 8:

      • Capstone Case Study: Designing an AI-Enhanced Workflow

      • Group Presentation: Tool Selection and Justification

      • Peer Feedback and Instructor Evaluation

      • Review of Trends and What’s Next in Workflow AI

  • Why take this course?

    This course explores how Artificial Intelligence (AI) technologies are being integrated into mental health care and behavioral support services. Students will examine existing AI tools used in diagnosis, therapeutic support, early intervention, and crisis management. Through hands-on exposure and discussions, learners will critically evaluate the benefits and risks associated with AI in this highly sensitive domain.

    Discover how AI is revolutionizing mental health care. From chatbots offering real-time support to predictive models flagging early signs of crisis, this course introduces students to cutting-edge tools and their real-world applications in psychology, social work, and behavioral health. Ideal for clinicians, social workers, and policy advocates.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: Introduction to Mental Health and AI

      ​

      • Overview of mental health systems and services

      • Introduction to AI applications in psychology and psychiatry

      • The role of technology in behavioural support

      • Opportunities and challenges of AI integration

      Session 2: Chatbots and Virtual Counselors

      • How conversational AI works (NLP, ML basics)

      • Examples: Woebot, Wysa, Tess, and Youper

      • Evaluating effectiveness and patient engagement

      • Limitations and safety concerns of AI-driven therapy

    • Session 3: Predictive Tools for Crisis Intervention

      • Machine learning models that detect suicidal ideation

      • Use of AI in triage and emergency response

      • Case studies in schools, clinics, and helplines

      • Ethical concerns in prediction without consent

      Session 4: Sentiment and Emotion Analysis

      • AI models that track mood and affect

      • Voice and facial emotion recognition

      • Passive monitoring through digital biomarkers

      • Data privacy and patient autonomy

    • Session 5: AI in Mental Health Diagnosis and Assessment

      • Natural Language Processing of clinical interviews

      • Screening tools for depression, anxiety, and PTSD

      • Neurodiversity and AI-driven cognitive assessments

      • Risks of over-reliance on algorithmic diagnosis

      Session 6: Personalized Interventions and Digital Therapies

      • AI-assisted behavioral nudges and habit tracking

      • Recommender systems for therapeutic content

      • Mobile apps and self-guided CBT platforms

      • Evaluating user outcomes and engagement levels

    • Session 7: AI in Social Services and Community Support

      • AI for social work caseload prioritization

      • AI-supported mental health outreach programs

      • Real-time analytics for shelter, housing, and addiction support

      • Interdisciplinary data sharing and ethics

      Session 8: Governance, Fairness, and the Future

      • Ensuring equity and inclusion in AI design

      • Informed consent, data ownership, and explainability

      • Policy frameworks and global standards

      • Trends, innovations, and responsible adoption

  • Why take this course?

    This 24-hour course teaches students to design and build conversational interfaces using modern chatbot and virtual assistant platforms. Through tools like Dialogflow, Botpress, GPT-4 API, and Tiledesk, students will create chat flows, integrate natural language processing, and connect chatbots to services or databases.

    Create bots that work for you. This practical course teaches you to design chatbots and AI assistants that can answer questions, complete tasks, and integrate with apps. From customer support to productivity tools, you'll explore how to build conversation-based systems without needing to code from scratch.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: Chatbot Foundations and Use Cases

      • Types of conversational systems: chatbots vs. agents vs. assistants

      • Business applications: support, onboarding, booking, info retrieval

      • Hands-on: Test examples from GPT, Dialogflow, and Intercom

      Session 2: Designing Intent-Based Flows

      • Intents, entities, and natural language understanding

      • Creating user paths and conditional logic trees

      • Hands-on: Build an FAQ bot with intent recognition

    • Session 3: Platforms for Chatbot Development

      • Overview of Dialogflow, Botpress, Tiledesk, and Landbot

      • Comparing hosted vs. on-premise options

      • Hands-on: Design a starter bot in Dialogflow or Botpress

      Session 4: Integrating APIs and Dynamic Data

      • Connecting bots to external systems: CRMs, Google Sheets, Zapier

      • Using webhooks and parameters to return live answers

      • Hands-on: Link a chatbot to fetch data from a spreadsheet or form

    • Session 5: Enhancing Conversations with Generative AI

      • Combining GPT-4 with chat interfaces for natural flow

      • Using prompt chaining and summarization

      • Hands-on: Add a generative AI fallback to a chatbot interface

      Session 6: Deploying Assistants Across Channels

      • Facebook Messenger, WhatsApp, web widgets, Slack

      • Multi-channel design considerations and handoff logic

      • Hands-on: Publish a chatbot to a messaging platform

    • Session 7: Ethics, Transparency, and User Trust

      • Disclaimers, data privacy, and limitations of automation

      • Fallbacks and human handoff design

      Session 8: Capstone – Build Your Conversational Assistant

      • Choose a use case: support bot, booking agent, onboarding tool

      • Build, test, and deploy your assistant with documentation

  • Why take this course?

    This course explores how Artificial Intelligence (AI) is transforming diagnostics and patient monitoring through advanced image and signal analysis. Students will gain an understanding of AI applications in radiology, pathology, and real-time biosignal interpretation such as ECGs and EEGs. Through hands-on exposure to tools and case studies, learners will evaluate the impact of AI on clinical workflows, diagnostic accuracy, and patient outcomes.

    Revolutionize the way diagnostics and monitoring are done! This course introduces AI-driven technologies enhancing medical imaging, pathology interpretation, and patient signal monitoring. Gain practical insight into real-world tools used in radiology labs and ICUs. Ideal for health professionals, medical imaging technologists, and healthcare IT innovators.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1:

      • Introduction to AI in Diagnostic Imaging

      • Types of medical imaging: X-ray, MRI, CT, Ultrasound

      • Overview of AI techniques in image recognition

      • Open-source tools (e.g., MONAI, PyTorch Medical Imaging Toolkit)

      • Real-world impact on radiology workflows

      Session 2:

      • Deep Learning for Image Classification and Segmentation

      • CNNs and transfer learning in medical images

      • Pathology slides and whole slide imaging (WSI)

      •  Segmentation tools (e.g., U-Net, nnU-Net)

      • Validation metrics: accuracy, sensitivity, specificity

    • Session 3:

      • Signal Analysis in Patient Monitoring

      • Introduction to biosignals: ECG, EEG, PPG

      • Time-series vs frequency domain representations

      • AI models for anomaly detection (arrhythmias, seizures)

      • MIT-BIH and PhysioNet datasets

      Session 4: 

      • Wearables and Remote Monitoring Devices

      • Data streams from smartwatches, fitness trackers, and patches

      • Integration with clinical decision systems

      • Noise filtering and signal preprocessing techniques

      • AI tools for activity and sleep pattern analysis

    • Session 5: 

      • Multimodal AI Models in Diagnostics

      • Combining images, signals, and clinical data

      • Fusion models and their architectures

      • Applications in stroke prediction and cardiac risk scoring

      • Case studies of multimodal platforms (e.g., IBM Watson, Aidoc)

      Session 6: 

      • Regulatory Standards and Dataset Bias

      • Bias and underrepresentation in imaging and signal datasets

      • Dataset annotation challenges in medical imaging

      • FDA approval pathways for AI diagnostic tools

      • Global regulatory considerations

    • Session 7:

      • Case Studies: AI in Radiology and Pathology

      • AI in breast cancer screening (mammography)

      • Lung disease detection from chest X-rays and CT

      • AI pathology in dermatology and gastrointestinal biopsies

      • Integration into PACS and hospital systems

      Session 8: 

      • Capstone Design: Diagnostic Workflow Enhancement with AI

      • Scenario-based project planning

      • Tool selection and data requirements

      • Workflow and performance benchmarks

      • Presentation and feedback session

  • Why take this course?

    This course provides a comprehensive exploration of the ethical, legal, and regulatory considerations surrounding the deployment of artificial intelligence in health care settings. As AI tools are increasingly integrated into diagnostics, treatment planning, administrative tasks, and patient interactions, understanding the complex landscape of accountability, privacy, fairness, and compliance becomes critical. Students will examine real-world use cases, emerging legislation, frameworks for responsible AI, and strategies to mitigate risks and ensure transparency and trust.

    Navigate the ethical and legal frontier of health care AI. This course helps professionals understand how to adopt AI tools responsibly—balancing innovation with privacy, equity, safety, and compliance in a rapidly changing regulatory environment.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: 

      • Introduction to ethics in AI and health care

      • Historical precedents and current concerns

      • Patient autonomy and informed consent

      • Overview of ethical frameworks (e.g., utilitarianism, deontology)

      Session 2: 

      • Key concepts: fairness, transparency, accountability

      • Case studies of ethical failures in medical AI

      • Stakeholders and competing interests

      • Frameworks for ethical AI development (e.g., OECD, WHO)

    • Session 3: 

      • AI bias and algorithmic discrimination in health care

      • Data quality and representational fairness

      • Examples of racially or gender-biased tools

      • Mitigation strategies and auditing techniques

      Session 4: 

      • Privacy laws and data protection (HIPAA, GDPR, PHIPA)

      • De-identification and data anonymization

      • Patient data ownership and consent management

      • Use of synthetic data and its limitations

    • Session 5: 

      • Legal liability for AI-driven decisions

      • Malpractice and standards of care in AI-assisted diagnostics

      • The role of clinicians vs. automated systems

      • Risk management strategies for health institutions

      Session 6: 

      • AI and regulatory bodies: FDA, EMA, Health Canada

      • Frameworks for approval and post-market surveillance

      • Challenges in regulating adaptive algorithms

      • Overview of pending AI Acts and proposals

    • Session 7: 

      • Transparency and explainability in AI models

      • XAI (explainable AI) tools in medical applications

      • Implications for trust and user adoption

      • Communication strategies for clinicians and patients

      Session 8: 

      • Future directions in AI governance and health equity

      • The role of interdisciplinary collaboration

      • Designing ethical AI for vulnerable populations

      • Capstone discussion: applying principles to a real-world scenario

Course Registration

Ready to take the next step? Browse our available and upcoming courses below and secure your spot today. Don’t miss out—enroll now to unlock new opportunities!

COURSE NAME
COURSE DURATION
COURSE FEE
REGISTRATION

Foundations of Agentic AI: Building Intelligent, Goal-Directed Systems

Month Year - Month Year

Session / Time

$xxxx

Registration Opens Month Year

Prompt Engineering for Beginners: Mastering the Language of AI

Month Year - Month Year

Session / Time

$xxxx

Registration Opens Month Year

Introduction to Generative AI

Month Year - Month Year

Session / Time

$xxxx

Registration Opens Month Year

Introduction to AI Tools: Exploring What’s Possible

Month Year - Month Year

Session / Time

$xxxx

Registration Opens Month Year

Experential Learning

By completing a Conifer 2C course, students gain exclusive access to upcoming charrettes, research projects, and pilot placement opportunities - bridging learning with real-world innovation.

jason-goodman-Oalh2MojUuk-unsplash (1).jpg

Charrette

1_NdxIFtI2AeW3WkTaFePRjA.jpg

Research Project

IT-Integration-scaled.jpg

Pilot Placement

AdobeStock_555641684.jpeg

Health Care & Services

Explore how artificial intelligence is reshaping health care and social services delivery. From predictive analytics and AI-assisted diagnostics to clinical documentation, workflow automation, and digital mental health support, this specialization connects patient-centered care with computational power. Students will gain hands-on experience with industry-leading platforms such as PathAI, Nuance DAX, and Olive AI, preparing them for roles in clinical innovation, care coordination, and equitable digital health delivery.

bottom of page