Machine learning is an application of Artificial Intelligence (AI) that allows computers to extract knowledge from data. Different than traditional line-by-line coding, machine learning (ML) algorithms use your data to learn relationships and patterns in it automatically to enable you to gain deeper insights into your data.
- Do you have data from your manufacturing processes, business operations, finance, or marketing that you want to analyze for insights?
- Are you interested in learning more about AI and ML but don’t know where to start?
- Do you want to learn how to apply powerful algorithms of ML to improve productivity, cost savings and profits?
Then, this course is for you!
In this course, we will show you how to use the ML algorithms rather than the theory behind them. You will learn the typical workflow (steps) to follow to use ML in your projects. You will apply ML using real-world datasets such as forecasting for sales, detecting faults in manufacturing lines or to pick stocks using the Dow Jones Industrial index.
Along the way, we will show you how to complete each step using Python and its Scikit-learn library. Previous knowledge of programming in Python is not required, but having some background in any programming languages will be helpful.
What you will learn
- Types of machine learning algorithms
- How to get data and how to prepare it for use in machine learning
- Setting up your software tools
- How to apply machine learning to solve real-world problems
Who should attend
Engineers, business managers, leaders, financial analysts, marketing practitioners, entrepreneurs, small businesses owners, non-profits, anyone who would like to gain insights into data using machine learning algorithms.
A typical course covers the following:
- What is Machine Learning?
- How does it work?
- Why should you learn Machine Learning?
- Programming tools
- Installing Anaconda and getting started with Jupyter Notebooks
- Overview of Python
- Basics of Python to get you going
- Workflow for Machine Learning
- Data pre-processing
- Picking the model that will be most suitable for the problem
- Training and testing the model
- Model evaluation and improvements
- Solving real-world problems using Machine Learning workflow
- Example 1: Forecasting daily orders using sales data
- Example 2: Semiconductor manufacturing line fault detection
- Example 3: Dow Jones index analysis to pick out stocks with similar variations
- Capstone presentations and wrap-up
Capstone projects are chosen by you at the beginning of the course. You are encouraged to bring your own dataset and apply ML to complete your project. The combination of the in-class training and the application of course concepts to a real-world capstone project will help increase your knowledge depth. You will receive one-on-one review of your project by the instructor.
Popular algorithms covered: (1) Supervised learning for regression, (2) Supervised learning for classification, and (3) Unsupervised learning for clustering.
Real-world datasets used: (1) Forecasting daily demand for orders, (2) Predicting employee productivity, (3) Semiconductor manufacturing line testing data, (4) Phone marketing campaign data for a bank, (5) Dow Jones Industrial stock market data, and (6) Online retail store sales data.
Applied learning model
- In- class examples and exercises focused on real-world applications
- A variety of “take home” problems to (optionally) practice between classes
- Course materials will be provided electronically
- All required software is available for download for free
Aref Majdara received his Ph.D. from Michigan Technological University. Currently, he is an assistant professor of Electrical Engineering at Washington State University Vancouver. He teaches a wide range of Electrical Engineering and Computer Science courses. His areas of interest include machine learning, density estimation, and embedded systems.
The instructional fee is $1,195. There are limited number of seats available for employees of small businesses with fewer than 150 employees ($598), non-profits and educational institutions ($299), and for current WSU students ($120) and employees ($299). All instructional fees are per person and include a nonrefundable administrative fee of $75. See Registration for details.
The registration fee is the same regardless of residence.
Meetings and format
The course is delivered via Zoom videoconference with live instruction. To attend, you need a computer with webcam, microphone and high-speed internet.
Each Zoom session allows for live interactions with the instructor and other students via chat, web conferencing or phone, all in real time. Assignments and other materials will be available online through a web-based learning management system.
- Attend at least six of eight classes