Guide to Machine Learning for Engineers
Credits: 8 PDH
PDH Course Description:
Machine learning is transforming the landscape of engineering and technology by enabling data-driven decision-making, automation, and innovation. This comprehensive course provides an overview of the structure, terminology, and practical applications in engineering practice, of machine learning. This course is designed as a guide for both novice, and intermediate levels of knowledge, with a final examination primarily designed to test the skills of the novice reader.As a novice on machine learning the reader can learn about the basic structure, terminology, and practical engineering applications used in this powerful computer modeling technology. As a more advanced reader, with a deeper prerequisite knowledge of computer programming principles, and more advanced statistical mathematics knowledge, this course delves deeper into the nuts and bolts of machine learning.
Topics:
- Introduction to Machine Learning
- Importance of Machine Learning in Engineering
- Key Concepts and Terminology
- Foundations of Machine Learning
- Data and its Types
- Supervised, Unsupervised, and Reinforcement Learning
- Feature Engineering
- Data Preprocessing, Data Collection and Cleaning
- Data Scaling and Normalization
- Neural Networks and Deep Learning
- Model Evaluation and Validation
- Machine Learning Applications in Engineering
- Ethical Considerations in Machine Learning
- Future Trends in Machine Learning
- Applications in Various Types of Engineering Practice
To take this course:
1.) Enroll in Course: Click below to enroll:
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3.) Test: Once you've thoroughly read the course materials, please click below to take the final examination.
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4.) Certificate: A passing grade of 70% or higher on the exam, is required to receive the certificate of completion for this PDH course.
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Intended Audience: This course is intended for engineers from all disciplines whose job description may require an introductory knowledge of machine learning and artificial intelligence principles and concepts.
Publication Source: Original content developed by Cadistics Courseware.
Donald Parnell, PE