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?

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.

Institution: UCSanDiegoX

Subject: Data Analysis & Statistics

Level: Advanced

Prerequisites:

The previous courses in the MicroMasters program: DSE200x and DSE210x

Undergraduate level education in:

Multivariate calculus

Linear algebra

Language: English

Video Transcript: English

Associated programs:

MicroMasters® Program in Data Science

Associated skills: Predictive Modeling, Data Science, Jupyter, Forecasting, Python (Programming Language), Algorithms, Unsupervised Learning, Machine Learning

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