Ementorhub

Skills you'll gain

Unsupervised Learning

Text Mining

Feature Engineering

Data Analysis

Dimensionality Reduction

NumPy

Data Mining

Big Data

Natural Language Processing

Algorithms

Scikit Learn (Machine Learning Library)

Machine Learning

Machine Learning Algorithms

Statistical Machine Learning

Linear Algebra

Data Science

There are 7 modules in this course

By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Distance Metrics & Computational Hurdles

Selecting a Clustering Algorithm

Dimensionality Reduction

Nonlinear and Distance-Based Dimensionality Reduction

Matrix Factorization

Final Project