This course is an intensive introduction to the most widely-used machine learning methods. It is part of the Data Science MicroMasters program provided by University of California San Diego. To earn the course certificate, I had to successfully complete ten weekly assignments and pass the proctored exam.
The course has three goals:
- Provide a basic intuitive understanding of the most popular machine learning methods:
- what they are good for
- how they work
- how they relate to one another
- their strengths and weaknesses
- Provide a hands-on feel for these methods through experiments with suitable data sets, using Jupyter notebooks.
- Understand machine learning methods at a deeper level by delving into their mathematical foundation. This is crucial to being able to adapt and modify existing methods and to creatively combining them.
I learned a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms:
- 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
The knowledge acquired from this course, enabled me to analyze many different types of data and to build descriptive and predictive models.
Credentials: