Applied Machine Learning

Course Description 

In the past decade, we have observed the expeditious evolution and tremendous application of machine learning, such as unmanned vehicles, autonomous language translation, and smart healthcare. This course will introduce the fundamental knowledge of machine learning techniques via a series of hands-on real-world examples in Python. The aim is to provide the students with a good understanding of machine-learning technologies, build machine learning with Python, and apply machine-learning technologies to address real-world problems. In the course projects, students will also have an opportunity to explore cutting-edge machine-learning technologies and develop their machine-learning-based solutions.

Note: Learners will be awarded a course competition certificate from Purdue University.

Learning Outcomes

At the end of this course, you should be able to:

• Explain the relationship (math mechanisms, internal logic, computing, components, and the usage constraints) of 8 machine learning models (Linear Regression, Logistic Regression, Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Recurrent Neural Network (Rnn), Autoencoder, general Adversarial Network (GAN), and Reinforcement Learning(RL).
• Describe the common operations in developing machine learning applications.
• Apply machine learning for manufacturing analytics.

Throughout the course, learners will be able to practice what they learned with a hands-on practice activity (ungraded). In the activities, the learners will be asked to program machine learning models with Python via the online platform https://colab.research.google.com/ A tutorial on how to use the online platform has been provided.

 Another tutorial demonstrating how to program a linear regression model has also been provided to enable an easier start for the learners. These activities are ungraded and will not count toward your grade. An example solution will be provided that demonstrates the correct programming.

Course Schedule and Duration

Below is the course schedule. It is recommended that you work through one module per week. The entire course is estimated to take 15 hours to complete.  The certificate is delivered within the Awards tool in the Brightspace course. *Schedule and assignments are subject to change. Any changes will be posted in Brightspace.

Target Audience

Sales engineers/business development executives, managers, manufacturing personnel, college students, and anyone interested in learning more about applied machine learning.