Learn, build and prepare
Start with machine learning concepts, build a working model, then learn the libraries behind modern language-model applications. These picks prioritize useful implementation skills over collecting badges.
Reviewed: · Published by BonusMantra
See the learning orderFollow the roadmap, choose relevant learning resources, build a project, then test your understanding with interview practice.
The sequence, role fit and project exercises are editorial recommendations. Provider requirements and certificate conditions are shown separately.
These resources offer free learning access. Some are guided tutorials rather than full courses. A free course does not necessarily include a certificate, hosted compute, private-repository features or a professional exam.
1. Google · Course
A focused starting point for understanding models, data and evaluation before building an AI application.
Compare two simple models on a held-out dataset. Explain which metric matters and investigate an error case.
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2. fast.ai · Course
A practical route from running a model to improving and sharing an application. Choose this when you learn best by building.
Create a small classifier, document the dataset limits and publish a reproducible inference demo.
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3. Hugging Face · Course
Useful for understanding the libraries and data preparation behind LLM applications rather than only calling a hosted chat endpoint.
Run a tokenizer and model pipeline on a small dataset; compare outputs and document how input length affects behavior.
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Pick one resource, complete a small exercise and explain the result aloud before adding another course.
