Decision Trees in ML
Learn what decision trees are, how they work, how impurity and information gain are used, and why they are important in machine learning.
AI Upskilling Program
Learn how AI systems work & how to build what wasn’t possible before with 6,000+ global learners.
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The syllabus
Start with the core foundations. Then you choose whether you want to train the models or build with them & the last two weeks bring both back together.
Shared Core
Weeks 1–7 · everyone
Machine Learning
Train the models → ML Engineer
AI Engineering
Build with models → AI Engineer
Advanced systems · capstone
Weeks 25–26 · both tracks
AI Glossary
Every field nests inside the one before it. Click any of the 89 terms to open its explainer, each has its own page with a plain-language definition.
Real World Projects
Don’t just learn AI, ship systems that work. Every stage of the program ends with something you can demonstrate, explain and add to your portfolio.
The community
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Stories
Six thousand learners, and these are the outcomes we hear about most: a first ML role, a promotion into a data team, or a career changed after a decade in something else.
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Writing
Learn what decision trees are, how they work, how impurity and information gain are used, and why they are important in machine learning.
Learn the five main types of AI agents: simple reflex, model-based reflex, goal-based, utility-based, and learning agents.
In simple words, it helps you see which variables move together, which move in opposite directions, and which have almost no relationship.
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