Ementorhub

What you'll learn

Analyze and differentiate between various machine learning algorithms, including unsupervised and supervised methods

Apply dimensionality reduction techniques, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), to complex datasets

Implement supervised learning algorithms using Python, and evaluate their performance through practical exercises and real-world case studies.

Develop and apply effective clustering methods to analyze and segment data

Skills you'll gain

Supervised Learning

Machine Learning

Predictive Modeling

Artificial Neural Networks

Feature Engineering

Regression Analysis

Unsupervised Learning

Dimensionality Reduction

Data Processing

Data Science

Scikit Learn (Machine Learning Library)

Classification And Regression Tree (CART)

Applied Machine Learning

Statistical Analysis

Machine Learning Algorithms

There are 5 modules in this course

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. The degree offers 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

Unsupervised Learning: Clustering

Supervised Learning: Regressions

Supervised Learning: Logistic Regression, Decision Trees, and SVMs

Supervised Learning: Neural Networks