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.
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.
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.
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
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.