Understand machine learning’s role in data-driven modeling, prediction, and decision-making. Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?
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In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.
Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.
Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.
All programming examples and assignments will be in Python, using Jupyter notebooks.
What you'll learn
- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
Prerequisites:
The previous courses in the MicroMasters program: Python for Data Science and Statistics and Probability in Data Science using Python
Undergraduate level education in:
- Multivariate calculus
- Linear algebra
