The supplied post names ten areas: LLMs, RAG, machine learning, deep learning, NLP, computer vision, MLOps, AWS and Azure AI, LangChain, and vector databases. Make the list actionable without treating “highest paying” as a promise.
The map displays ten core AI competencies. Selecting a card activates an orange focus ring and updates your priority in browser storage. Checking Completed for a topic recalculates your total progress count, expanding the progress bar from zero up toward ten completed skills. Typing notes into the card streams evidence directly into the local state object, preserving your proof of work across browser sessions. Clicking Export compiles your chosen focus, completed list, and notes into an immediate JSON download for portfolios and career reviews.
What does marking a skill completed actually establish?
This map organizes ten topics from the supplied post. Selecting a focus highlights one topic; checkboxes count your own completion decisions. Notes let you record what you built or what you will try next. The counter measures checked topics, not tested competence or employability.
Topics overlap and are not an ordered curriculum. The page assigns no prerequisites, scores, salaries or difficulty levels. Completion is self-reported and all ten topics contribute equally to the counter. A tool or framework name is not a substitute for understanding the underlying concepts.
Start with zero completed topics. Choose Machine Learning as your focus, then write a note such as: trained a baseline classifier, kept a separate test set and explained its errors. Check its Completed box: the counter becomes 1 of 10 complete. This records your decision; the page does not inspect the project. Export the map to preserve the note and choose a concrete next exercise.
Google’s Machine Learning Crash Course lists programming and mathematical preparation, including Python familiarity, algebra and statistics, and provides NumPy and pandas prework. Use those prerequisites to identify gaps before a machine-learning exercise. They describe that course’s preparation, not a required order for every topic in this map. Google Machine Learning Crash Course: prerequisites and prework
In your note, name the task, a project or artifact location, how you checked the result and one limitation you found. For RAG, an example is a small document collection, test questions, inspected retrieved passages and cases where the answer lacked support. These are suggested review criteria; checking the box does not certify that they were met.
The export contains the topic list, selected focus, completed topic names, notes, creation time and the page’s scope statement. Entries are saved using browser local storage rather than an account-sync service. MDN explains that this storage belongs to the document’s origin and that private-browsing data is cleared when the last private tab closes. Export before clearing browser data. Reset clears the map’s choices and notes. MDN: browser localStorage
Practical preparation for this course, including programming and mathematical foundations; no salary or job guarantee.
Explains origin-scoped persistence and private-browsing lifetime; supports the storage caveat.