See what people in AI really do, find your starting point, including the experience you already have, and make a realistic plan to get there.
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By the end of this course, you will be able to:
Many people want to “work in AI”, but AI work is not one job. Some people build data pipelines, some train models, and some build the application that users see. Others decide what to build, test whether it works, or check that it is safe and legal. This course shows what each of these people does on a normal day, and how they work together on one project.
You do not start from zero. Maybe you studied or worked in another field, such as finance, health care, law or retail. Then you know things that an AI team needs and cannot easily learn. The course shows where that knowledge matters, and how people with it contribute to AI products and lead them.
At the end, you write a career transition brief: your target role, an honest list of your skills and gaps, a weekly study plan, and two portfolio project ideas. You do not need to write code in this course. You need a browser and a text editor.
None. You need a browser and a text editor, and you must be able to make a folder and save a file. You do not write code in this course.
Fresh graduates, software developers, analysts and professionals from any other field who want to work in AI. If you have years of experience in finance, health, law, retail or another field, this course shows how that experience counts.
Experienced AI practitioners designed this course. AI helped us draft the content; people chose the teaching approach and the examples, and reviewed every lesson. The outputs that you see are the actual results of running the code. How we make our courses
Follow one AI product from the user's need to daily operation, and meet the people who build it. You compare the main AI roles by what they produce, and see how a team divides the work, including on the day something goes wrong.
Find out what you can already do. You learn why experience in another field is a head start in AI, see what domain knowledge means in finance, health, legal, retail and other fields, and choose a realistic role from evidence, not from a guess.
Plan a small AI project the way a team does. You frame the problem before you choose any tool, start from a simple baseline, and decide how to measure success and what to do when the AI is wrong.
Turn your goal into a study plan that you can keep. You read the curriculum's prerequisites, plan practice that builds real skill, and keep your tools and costs under control.
Show your work so that an employer can trust it. You plan a portfolio project, explain your decisions honestly, and link every skill that you claim to evidence.
Use every module in one practice project. You collect evidence, and check it against the course outcomes.
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