Ementorhub

Skills you'll gain

Dimensionality Reduction

Feature Engineering

Scikit Learn (Machine Learning Library)

Algorithms

Machine Learning

Text Mining

Data Science

Machine Learning Algorithms

NumPy

Data Mining

Natural Language Processing

Unsupervised Learning

Statistical Machine Learning

Data Analysis

Linear Algebra

Big Data

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