Brief Introduction
Understand machine learning's role in data-driven modeling, prediction, and decision-making.
Description
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.
Knowledge
- 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
Data Science for Professionals
- 4.3
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It's time to leave spreadsheets behind. . . What is it? Data Science for Professionals is simply the best way to gain a in-depth and practical skill set in data science....
MLOps (Machine Learning Operations) Fundamentals
- 4
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud....
Fundamentals of Scalable Data Science
- 4.3
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Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization....
ML Pipelines on Google Cloud
- 3.7
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In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud....
