Ementorhub

What you'll learn

Explain what unsupervised learning is, and list methods used in unsupervised learning.

List and explain algorithms for various matrix factorization methods, and what each is used for.

Skills you'll gain

Linear Algebra

Scikit Learn (Machine Learning Library)

Data Science

Statistical Analysis

Dimensionality Reduction

Machine Learning

Machine Learning Algorithms

Unsupervised Learning

NumPy

Exploratory Data Analysis

Python Programming

There are 4 modules in this course

Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. College-level math skills, including Calculus and Linear Algebra, are needed. It is recommended, but not required, to take the first course in the specialization, Introduction to Machine Learning: Supervised Learning. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image by Ryan Wallace on Unsplash.

Clustering

Recommender System

Matrix Factorization