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Placements in Artificial Intelligence: 1,342
Artificial Intelligence Course in Canada with Certification
Learn agentic AI and generative AI with practical training in Canada. You will work with OpenAI API, LangChain, LangGraph, Hugging Face, Pinecone, FastAPI, Docker, Playwright, Selenium, and structured tool calling to build useful AI workflows end to end.
4.7/5 from 1,432 reviews
Build agentic AI workflows with ReAct, planning, and multi-agent patterns.
Train on OpenAI API, LangChain, LangGraph, AutoGen, and CrewAI.
Work on hands-on projects like research agents, data analyst agents, and chatbot deployment.
Learn memory, guardrails, evals, observability, and production deployment.
Get certification guidance, resume support, and placement assistance for AI roles in Canada.
Job Interview
Guarantee Program
14,200+ (Placed)
Freshers to IT
7,100+ (Placed)
Non-IT to Tech
5,800+ (Placed)
Career Gap Fillers
6,400+ (Placed)
Upskilling Success
24,999₹
In 60 Days + Placement
Course Fee:₹24,999
Duration:60 Days
Mode:Classroom & Online
Free Session
1 Hour Training Session
Get Job with our Guaranteed Placement Support Program
Placement Assistance for Artificial Intelligence Careers in Canada
Learning AI is only one part of the job search. Inventateq supports you with the practical steps needed to present your skills clearly for entry-level and experienced AI roles in Canada, from project framing to interview preparation.
Our Signature Career Support:
Resume help focused on AI, data, and automation roles such as Data Analyst, Business Analyst, and AI Engineer.
Portfolio support for agentic AI, generative AI, and deployment projects built during training.
Mock interview practice for tool use, prompt design, Python workflows, and project explanation.
Career mentoring to map your skills to roles like Junior Data Analyst, Analytics Consultant, and Business Intelligence Analyst.
Guidance on presenting certification, projects, and tools used across the course in a job-ready format.
Artificial Intelligence Salary Insights in Canada
AI and analytics roles in Canada are hired across IT services, product companies, consulting, finance, operations, and data-driven business teams. Pay rises with project ownership, deployment skills, and experience working with real AI tools and workflows.
Artificial Intelligence Average Salary by Experience
Artificial Intelligence Salary Insights in Canada
AI and analytics roles in Canada are hired across IT services, product companies, consulting, finance, operations, and data-driven business teams. Pay rises with project ownership, deployment skills, and experience working with real AI tools and workflows.
Artificial Intelligence Average Salary by Experience
Why Students Choose Our Artificial Intelligence Course in Canada?
4.7/5 Google Rating | 1,432+ Verified Reviews
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Success Result:Our students are competing at global levels. Watch their placement journey here.
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About Inventateq Artificial Intelligence Training Institute in Canada
Inventateq teaches artificial intelligence through structured practice, not just theory. The syllabus moves from agent fundamentals and prompt engineering into tools, memory, frameworks, multi-agent systems, guardrails, evaluation, and deployment, so learners understand how modern AI systems are actually built.
We stand apart through our commitment to:
Learn to build agentic AI systems with real tool use and structured outputs.
Practice with LangChain, LangGraph, AutoGen, CrewAI, OpenAI API, and Hugging Face.
Work on projects that cover research agents, data analysis, and chatbot deployment.
Get mentor support while you move from basic concepts to production-style workflows.
Choose training that fits classroom, online, weekday, or weekend learning needs.
Live Online
Remote Learning
AI Online Live Classes
Live online classes bring the same AI training to learners across Canada. You join mentor-led sessions in real time, complete assignments, and work through tools and projects without needing to travel. The format is practical for working professionals and students who need flexibility.
Live interactive classes with practical coding demonstrations
Recorded sessions available for revision and practice
Weekly assignments with mentor feedback and guidance
Real projects covering Generative AI, LLMs, and Agentic AI
Online career guidance and interview preparation support
Artificial Intelligence Training Program
Fresh graduates
Good for students who want practical AI skills before applying for analyst, automation, or junior AI roles.
Working professionals
Useful for IT, analytics, and business professionals who want to move into agentic AI and generative AI work.
Data and BI learners
Fits learners already using Python, SQL, Excel, Power BI, or Tableau who want stronger AI project skills.
Career switchers
A solid path for non-AI candidates who want structured training and guided project work.
Anyone interested in AI tools
Suitable for learners who want to understand OpenAI API, LangChain, vector databases, and deployment.
Quick Highlights of Inventateq Artificial Intelligence Course
Course Duration
Duration: Structured into weekly modules with project work across the program.
Mode: Available in classroom and live online formats.
Training style: Mentor-led sessions with hands-on labs and assignments.
Focus: Agentic AI, generative AI, deployment, and workflow building.
No prior AI experience is needed to start this course.
Artificial Intelligence Curriculum
1. Introduction to Agentic AI (Week 1)
W1
•Trace the path from NLP to LLMs, chatbots, and agents.
•Define agency through autonomy, goal direction, and tool use.
•Understand the LLM as the brain with perception and action around it.
•Learn the ReAct pattern and the main risks such as hallucinations, cost, and latency.
2. Prompt Engineering for Agents (Week 2)
W2
•Write system prompts for role-based behavior.
•Use chain-of-thought and tree-of-thought approaches for agent reasoning.
•Work with structured output using JSON mode and function calling.
•Compare XML tagging and JSON mode for clearer agent instructions.
3. Tools and Function Calling (Week 3)
W3
•Define tools and functions with schemas.
•Use parallel function calling where tasks can run together.
•Choose tools based on task needs and workflow design.
•Handle tool errors without breaking the agent flow.
4. Memory Architectures (Week 4)
W4
•Manage short-term memory in the context window and long-term memory in vector databases.
•Use semantic memory for retrieval-augmented agent behavior.
•Store episodic memory from past actions and outcomes.
•Handle state across loops in long-running agent workflows.
5. Agentic Frameworks (Week 5)
W5
•Review LangChain, LangGraph, AutoGen, and CrewAI.
•Understand the trade-offs between chains, agents, and graphs.
•See where each framework fits in real AI projects.
•Port a basic ReAct loop into LangGraph in a hands-on exercise.
6. Planning and Complex Workflows (Week 6)
W6
•Build plan-and-execute architectures for multi-step tasks.
•Use reflexion for self-critique and iteration.
•Study LLMCompiler for optimized parallel execution.
•Work with dynamic workflows and compare them with DAG-based design.
7. Multi-Agent Systems (Week 7)
W7
•Design systems where specialized agents work together.
•Compare hierarchical, peer-to-peer, and sequential handoff architectures.
•Set communication protocols between agents.
•Use AutoGen and CrewAI for role-based and conversable agent setups.
8. Human-in-the-Loop and Guardrails (Week 8)
W8
•Add breakpoints and interrupts into autonomous workflows.
•Use approval steps before destructive actions.
•Apply input and output guardrails with tools like NeMo Guardrails and Guardrails AI.
•Control budget by limiting token spend and loop iterations.
9. Evaluation and Observability (Week 9)
W9
•Understand why agent evaluation is difficult because of non-determinism.
•Compare trajectory evaluation with final outcome evaluation.
•Use LangSmith and Phoenix Arize for tracing and analysis.
•Track latency, token usage, and failure points inside graphs.
10. Advanced Architectures (Week 10)
W10
•Study autonomous agent patterns such as BabyAGI and AutoGPT.
•Use code-as-action where agents write and run Python in a sandbox.
•Explore multimodal agents with vision and audio.
•Build web navigation agents using Playwright or Selenium.
11. Data Analyst Agent Project (Week 10)
W10
•Create an agent that reads a CSV file and generates analysis.
•Let the agent write Python code through a REPL tool.
•Generate visualizations autonomously from the dataset.
•Turn the workflow into a practical analytics project.
12. Production Deployment (Week 11)
W11
•Deploy agents with streaming and blocking response modes.
•Use AsyncIO for asynchronous agents.
•Containerize agent sandboxes with Docker.
•Wrap the system in FastAPI and deploy to cloud platforms like Render or AWS.
13. Generative AI Course Foundation (Week 12)
W12
•Introduction to Generative AI and the basics of OpenAI API.
•Start LangChain and build early workflow applications.
•Use Hugging Face API and LangChain applications.
•Practice with memory in LangChain and prepare for a project build.
14. Vector Databases and Open Source LLMs (Week 13)
W13
•Work with vector databases for AI and LLM applications.
•Use Pinecone for vector search and retrieval.
•Introduce open-source LLM models for practical use cases.
•Connect retrieval and memory concepts to chatbot work.
15. End-to-End Chatbot Project and Deployment (Week 14)
W14
•Build an end-to-end medical chatbot in two parts.
•Move from creation to deployment of the chatbot.
•Use buffer time for revision and project completion.
•Bring together model use, retrieval, and deployment in one workflow.
Rated 4.9/5
Why Inventateq for Artificial Intelligence Training in Canada?
Inventateq focuses on practical AI training that follows the way real projects are built. You learn the tools, workflows, and deployment steps in a guided sequence so the concepts stay connected to job work.
Why Students Trust Inventateq Canada
Trainers explain AI topics with practical examples and clear step-by-step teaching.
The curriculum stays close to current agentic AI and generative AI workflows.
Students get support while building projects, not only after the class ends.
The learning environment is structured for beginners as well as working professionals.
Course delivery is flexible with classroom and live online options.
Build Real Artificial Intelligence Skills for Career Growth
This course gives you practice with the tools and workflows used in modern AI projects. By the end, you will have hands-on experience with agents, memory, guardrails, evals, and deployment.
Build agentic AI workflows
Learn how to move from prompts to agents that can use tools, plan tasks, and handle multi-step actions with structured logic.
Create practical AI projects
Work on projects such as a research agent, a data analyst agent, and an end-to-end medical chatbot.
Use real development tools
Practice with OpenAI API, LangChain, LangGraph, Hugging Face, Pinecone, FastAPI, Docker, and cloud deployment tools.
Understand memory and retrieval
Learn how short-term memory, long-term memory, and RAG support more reliable AI workflows.
Handle safety and control
Add human approval, guardrails, and budget checks so agents behave in a controlled way.
Prepare for job discussions
Build the confidence to explain your project design, tool choices, evaluation approach, and deployment decisions.
Artificial Intelligence Training Certification
The certification shows that you completed a structured AI program covering agent design, generative AI, memory, evaluation, and deployment. It helps employers see that you have worked with real tools and projects rather than only studying theory.
OpenAI API and structured tool calling
Earn this certificate upon successful completion of our training program.
LangChain, LangGraph, AutoGen, and CrewAI
Validate your skills with recognized industry credentials.
Pinecone vector database and Hugging Face API
Earn this certificate upon successful completion of our training program.
FastAPI, Docker, and cloud deployment workflows
Validate your skills with recognized industry credentials.
Detailed Insights: Artificial Intelligence Course in Canada
Students Frequently Asked Questions
Is this Artificial Intelligence course suitable for beginners?
Yes. The course starts with the evolution from NLP to agents and then builds into tools, memory, frameworks, and deployment. Beginners can follow the sequence step by step. The mentor-led format also makes it easier to understand each concept before moving to the next one.
Will I work on hands-on AI projects?
Yes, the course includes practical work throughout the syllabus. You will build projects such as a research agent, a data analyst agent, and an end-to-end medical chatbot. These projects help you understand how the tools connect in a real workflow. They also give you material for your resume and interviews.
Do you provide placement assistance after the course?
Yes, placement support is part of the training approach. Inventateq helps with resume preparation, project presentation, mock interviews, and role mapping. The support is designed to help you apply for AI and analytics roles with confidence. It does not replace effort, but it gives you clear guidance.
Can non-technical students join this AI training?
Yes, many learners can start without a deep AI background. The course explains each stage from the basics and then builds toward more advanced topics. A willingness to practice Python-style workflows, prompts, and tools is important. If you are coming from analytics or business roles, the course can still be a strong fit.
Is online training available for learners in Canada?
Yes, live online training is available. You can attend mentor-led classes from your location in Canada and still follow the same practical syllabus. The online format is useful if you want flexibility without missing project guidance. It is suitable for working professionals and students alike.
How long is the course and what mode is available?
The program is structured into weekly modules with dedicated project work. It is available in classroom and live online modes. The exact pace can vary by batch, but the content is organized so you can follow it in a practical sequence. This makes it manageable for both full-time learners and working professionals.
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