Overview

Machine learning and deep learning methods drive modern data analysis across many healthcare, education, and engineering applications. In this highly interactive course, you’ll gain insights into how to use descriptive analysis to understand your data, what kinds of problems these methods can and cannot solve, how to build and train deep neural networks as well as identify its key architecture parameters, and what issues are likely to arise in practical applications.

Learning Outcomes

Understand your data and draw insights from your data sets.

Gain an understanding of machine learning and deep learning building blocks.

Develop a practical and hands-on experience of machine learning and deep learning concepts and their usage in building computing systems for addressing a wide variety of problems across different disciplines.


Who Should Attend

The participants will gain a practical understanding of the methods and techniques used in machine learning and deep learning applications. This course is intended for mid-career technical, scientific, business, and government professionals. 


Outline

Module 1: Introduction to Machine Learning

Module 2: Understanding Your Data

Module 3:  Supervised Learning: Regression Methods

Module 4:  Supervised Learning: Classification Methods

Module 5:  Unsupervised Learning: Clustering Methods

Module 6:  Unsupervised learning: Dimensionality Reduction Methods

Module 7:  Introduction to Neural Networks

Module 8:  Deep Learning for Images Analysis

Module 9:  Deep Learning for Text Analysis

 

Requirements

Laptops with Python installed are encouraged, but not required for this course (all coding will be done in a browser).

Participants should have basic experience in Python programming language. There are no prerequisites in math or computational science, although a basic understanding of statistics is helpful.