10 BEGINNER AI COURSES
WITH CERTIFICATES FOR FIRST-TIME LEARNERS

Generative AI Training Course (Great Learning)

This is a short, self-paced certificate program. You learn prompt patterns, output checks, and safe-use basics for day-to-day tasks. You finish with a shareable certificate and a brief workflow write-up you can post on LinkedIn.

Key highlights

• Practical focus: Clear prompt templates for drafting, analysis, and QA with simple guardrails.
• Real tasks: Build a before-and-after workflow that shows time saved or quality gains.
• Tool agnostic: Concepts apply across common AI tools used at work.
• Easy support: Guided checklists reduce trial and error for beginners.
• Shareable proof: Great Learning certificate plus a concise artifact for your profile.

AI for Everyone (Great Learning)

This is a short, self-paced overview with a certificate. You learn core AI terms, common use cases, limits, and responsible adoption. You produce a one-page opportunity brief tailored to your team.

Key highlights

• Plain English: Concepts explained without heavy math or code.
• Business relevance: Examples from marketing, operations, and support.
• Risk basics: Simple guidance for safe use and stakeholder trust.
• Action templates: Worksheets to map two safe use cases in your role.
• Shareable proof: Great Learning certificate and a brief you can attach to applications.

Free AI Agents Course (Great Learning)

This is a short, self-paced, free course with an optional certificate. You learn agent concepts, tool calling, and simple step chaining for routine tasks. You complete a tiny agent demo and a short explainer you can share.

Key highlights

• Beginner-friendly: Agent patterns taught without heavy coding.
• Task focus: Plan, execute, and verify small automations end-to-end.
• Safety first: Checklists to monitor outputs and reduce mistakes.
• Reusable setup: Templates you can adapt for new tasks at work.
• Shareable proof: Completion certificate plus a working demo link.

AI for Everyone (Coursera - Andrew Ng)

This is a short, self-paced course with a certificate option. You learn practical AI strategy, team roles, and what AI can and cannot do. You finish with a Coursera certificate and notes you can reuse in proposals.

Key highlights

• Trusted instructor: Clear teaching from Andrew Ng with industry context.
• Strategy lens: Understand value, feasibility, and risks for your projects.
• Case reflections: Short prompts to apply ideas to your workplace.
• Steady pacing: Quizzes and modules support retention.
• Shareable proof: Coursera certificate that fits well on LinkedIn.

Introduction to Generative AI (Google Cloud Skills Boost)

This is a very short, self-paced badge course. You learn GenAI basics, LLM concepts, and how GenAI differs from traditional ML. You earn a Google Cloud skill badge that you can add to your profile.

Key highlights

• Credible source: Material created by Google Cloud educators.
• Clear scope: Definitions and key terms for quick understanding.
• Fast checks: Short quizzes aligned to workplace vocabulary.
• Busy-friendly: Learn-in-bites format fits tight schedules.
• Shareable proof: Digital badge recognized by cloud employers.

Career Essentials in Generative AI (Microsoft + LinkedIn Learning)

This is a self-paced learning path with a completion certificate. You learn how GenAI supports roles across the workplace, with attention to ethics and day-to-day patterns. You get a LinkedIn certificate and practical talking points for interviews.

Key highlights

• Enterprise view: Content shaped by Microsoft and LinkedIn teams.
• Workplace focus: Scenarios that match common office tasks.
• Responsible use: Simple principles you can follow and explain.
• Easy format: Short videos, worksheets, and quick quizzes.
• Shareable proof: Certificate added directly to your LinkedIn profile.

Introduction to Generative AI and Agents (Microsoft Learn)

This is a single-module, self-paced unit with a profile badge. You learn LLM fundamentals, prompt basics, and what makes an agent useful. You finish with a Microsoft Learn badge and reference notes.

Key highlights

• Official content: Clear learning objectives from Microsoft Learn.
• Foundational terms: Concepts that prepare you for Azure AI tracks.
• Knowledge checks: Small tasks to cement patterns and wording.
• Next steps: Links to deeper modules when you are ready.
• Shareable proof: Profile badge that verifies your completion.

AI Engineering Professional Certificate (IBM on Coursera)

This is a multi-course professional certificate. You learn ML foundations, intro to deep learning, and common tools used by AI teams. You complete several small projects and earn a professional certificate.

Key highlights

• Industry input: IBM experts focus on tools used by teams today.
• Structured path: From basics to applied labs across frameworks.
• Portfolio build: Multiple mini projects that show breadth.
• Peer review: Graded labs and feedback improve your work.
• Shareable proof: Coursera professional certificate that signals commitment.

AI Fundamentals (Udacity)

This is a short, self-paced foundational course. You learn core ideas across ML, vision, and language in plain terms. You finish with a completion certificate and a clear vocabulary for the next steps.

Key highlights

• Concise teaching: Instructors keep explanations tight and practical.
• Broad survey: Overview helps you choose your next path.
• Light practice: Exercises without heavy math or code.
• Flexible pacing: Modular lessons fit busy schedules.
• Shareable proof: Completion certificate plus baseline terminology.

Generative AI Learning Plan for Developers (AWS Training)

This is a curated, self-paced learning plan. You learn how to use LLMs on AWS, key Bedrock concepts, and safe patterns for simple apps. You are complete with badges and preparation for entry-level AWS AI credentials.

Key highlights

• Cloud context: Trainers connect GenAI to services used in industry.
• Developer path: Modules introduce real API workflows and tools.
• Guided labs: Hands-on steps to build practical confidence.
• Role-based plan: Checkpoints and follow-on tracks keep progress steady.
• Shareable proof: Badges and exam readiness for AWS AI credentials.

How to pick the best courses

This is a curated, self-paced learning plan. You learn how to use LLMs on AWS, key Bedrock concepts, and safe patterns for simple apps. You are complete with badges and preparation for entry-level AWS AI credentials.

• Match your goal. Want better reports? Try a visualization course plus a GenAI intro. Want automation? Try agents plus a cloud path.
• Plan your time. Set aside 5 to 7 hours a week. Put one session on your calendar and keep it.
• Create proof. Aim for one shareable artifact per course. A dashboard, a prompt library, a brief, or a tiny agent demo.
• Stack smart. Start with literacy, add a hands-on course, then pick one tool track you can use at work.
• Verify certificates. Look for badges or certificates you can link to on LinkedIn and your resume.

Conclusion

You do not need a long degree to show progress in AI. Take two of these beginner courses. Finish them this month. Share one artifact for each and add the certificates to your profile. If the budget is tight, start with online free courses with certificate that still offer badges or completion proof. Small, consistent steps will move you from curious to capable.