Create machine learning models

AI Engineer
Data Scientist
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Microsoft Learn provides several interactive ways to get an introduction to classic machine learning. These learning paths will get you productive on their own, and also are an excellent base for moving on to deep learning topics.

From the most basic classical machine learning models, to exploratory data analysis and customizing architectures, you’ll be guided by easy to digest conceptual content and interactive Jupyter notebooks, all without leaving your browser.

Choose your own path depending on your educational background and interests.

Option 1: The complete course: Foundations of data science for machine learning

This path is recommended for most people. It has all the same modules as the other two learning paths with a custom flow that maximizes reinforcement of concepts. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. It's also the best path if you plan to move beyond classic machine learning, and get an education in deep learning and neural networks, which we only introduce here.

Option 2: The Understand data science for machine learning learning path

If you are looking to understand how machine learning works and don't have much mathematical background then this path is for you. It makes no assumptions about previous education (other than a light familiarity with coding concepts) and teaches with code, metaphor, and visual that give you the ah ha moment. It's hands-on, but focuses more on understanding fundamentals and less on the power of the tools and libraries available.

✔ Option 3: The Create machine learning models learning path

If you already have some idea what machine learning is about or you have a strong mathematical background you may best enjoy jumping right in to the Create Machine Learning Models learning path. These modules teach some machine learning concepts, but move fast so they can get to the power of using tools like scikit-learn, TensorFlow, and PyTorch. This learning path is also the best one for you if you're looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks.

✔ You are currently on this path, scroll down to begin.


This learning path assumes knowledge of basic mathematical concepts. Some experience with Python is also beneficial.

Modules in this learning path

Data exploration and analysis is at the core of data science. Data scientists require skills in languages like Python to explore, visualize, and manipulate data.

Regression is a commonly used kind of machine learning for predicting numeric values.

Classification is a kind of machine learning used to categorize items into classes.

Clustering is a kind of machine learning that is used to group similar items into clusters.

Deep learning is an advanced form of machine learning that emulates the way the human brain learns through networks of connected neurons.