Machine Learning
Data Management
Train and Evaluate
Productize It
Hands-On
Guidelines
The course is organized in four lectures that cover the full life-cycle of an AI & ML product from the research and formulation of the business idea, how initial and user data is collected, curated, and standardized, so it can be fed back to the system, what the tools that are available to train and evaluate your ML models are, followed by best practices when deploying and monitoring your model.
Each lecture contains a series of short and practical lessons, along with some recommended readings at the end.
Tools
This course is taught in an interactive environment hosted in Gitbook that lets learners exchange opinions, rate the content, and share their thoughts with the instructors and whole community.
In order to achieve that, a really important part of this course does not only live in the content pages exclusively but also in its community (join the Discord community if you have not done it yet!
) and page comments.
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Contents
Tools