Professional and Continuing Education
Pawley Hall, Room 440G
456 Pioneer Drive
Rochester,
MI
48309-4482
(location map)
(248) 370-3177
oupace@mxappagd.com
![Machine Learning banner A man with two laptops and desktop in front of him](/Assets/Oakland/pace/graphics/banners/Machine%20Learning%20Web%20Banner.jpg)
Data Analytics and Machine Learning (ML Ops) on the Cloud
Sign up to receive emails about OU's Machine Learning Program.
Course Description:
Machine Learning as a field is now incredibly pervasive, with applications in areas including business intelligence, homeland security, biochemical interaction analysis, infrastructure monitoring, and astrophysics. Deep learning is a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance of a give task. Deep learning is behind many recent advances in AI, including Siri’s speech recognition, Facebook’s tag suggestions, machine language translation and self-driving cars. This course is an introduction to Machine Learning using TensorFlow 2.0, which is a very popular framework for building predictive models. The course will provide a step by step approach to building complex machine learning models starting from the very basics concepts of machine learning and the TensorFlow 2.0 framework from Google. We will be using a variety of tools and platforms such as Python, TensorFlow/Keras, and Google Collaboratory Notebooks for building, testing, and deploying machine learning models.
This 7-week program contains 42 contact hours of online, synchronous instruction and is broken into 3 modules and covers fundamental topics exposing students to Artificial Intelligence and Machine Learning. The program is ideal for graduating and working engineers new to the Artificial Intelligence and Machine Learning world.
This program contains specializations for Retail, Healthcare, Financial Services and Industrial / Manufacturing. You can select one or more specializations as part of the course (each specialization is 3-5 weeks long). You will understand the use cases defined below and implement one use case end-to-end as a part of your project.
A PACE Certificate of Achievement will be awarded upon successful completion of the program.
REGISTER
Learning Outcomes
By the end of the course, students will be able to:
- Explain how machine learning models work
- Frame tasks into machine learning problems
- Use machine learning toolkits to implement the designed models
- Justify when and why specific machine learning techniques work for specific problems
- Build, test, and deploy complex machine learning models to solve specific problems
Tentative Course Outline
Each module contains corresponding hands-on labs covering module topics.
- Python Tutorial, including:
- Data Types & Strings
- User Defined Functions
- Pandas Series
- Lambda & Map
- Introduction to Classes and Objects
This module covers the fundamentals of Python programming. After completing this module, students will be able to write reasonably complex Python code for working with data. The following table shows the topics covered in this module.
Python Basics
Topics | Details |
---|---|
Python Basics |
|
Data structures |
|
Using Modules |
|
Introduction to Classes and Objects |
|
Files, streams, database connectivity and API |
|
- Introduction to Machine Learning
- What is Machine Learning?
- Introduction to TensorFlow
- Building TensorFlow Models
- Scaling-up and Model Deployment
This module focuses on the fundamentals of machine learning and the commonly used ML and Deep Learning models on the Google Cloud platform. Building models using TensorFlow, training and assessing their performance using TensorBoard, and deploying the models will be discussed.
Machine Learning using TensorFlow 2.0
Topics | Details |
---|---|
Introduction to Machine Learning |
|
What is Machine Learning? |
|
Introduction to TensorFlow |
|
Building TensorFlow Models |
|
Scaling-up and Model Deployment |
|
Labs for Module-2
Lab1: Implement a Linear Regression and KNN Model.
Lab2: Create a model using TensorFlow - Feature Engineering for a DNN Model
Lab3: Improve the model performance using Feature Engineering.
Lab4: Deploy the TensorFlow model using Flask API.
This module is industry specialization for Retail, Healthcare, Financial Services and Industrial / Manufacturing. Students can select one or more specializations as part of the course. This module will focus on the use cases defined below, and students will implement one use case end-to-end as part of the capstone project.
Industry Focus | Use Cases |
---|---|
Retail |
|
Healthcare |
|
Financial Services |
|
Industrial / Manufacturing |
|
Labs for Module-3
Lab1: Understand a business problem and implement an exploratory data analysis using Python.
Lab2: Create a machine learning model using TensorFlow
Lab3: Improve the model performance using Feature Engineering
Lab4: Deploy the TensorFlow model using Flask API
Instructor Information
Name: Vijayan Sugumaran
Title: Distinguished Professor of Management Information Systems
Contact Information: sugumara@mxappagd.com
Name: Naresh Jasotani
Title: Specialist Customer Engg. (AI / ML, Data & Analytics)