台北(02)2773-8111/台中(04)2326-1722/高雄(07)311-2280 | LINE: @idptw
關於課程
This professional development course is designed for engineers and technicians who need to harness machine learning technologies in their engineering work or become better problem solvers through the application of machine learning. This course will teach you to use Python Programming to work with machine learning applications and to apply supervised and unsupervised machine learning to engineering problems.
Receive a Certificate of Completion from EIT.
Learn from well-known faculty and industry experts from around the globe.
Flexibility of attending anytime from anywhere, even when you are working full-time.
Interact with industry experts during the webinars and get the latest updates/announcements on the subject.
Experience a global learning with students from various backgrounds and experience which is a great networking opportunity.
Learn to use Python Programming to work with machine learning applications and to apply supervised and unsupervised machine learning to engineering problems.
Study the fundamentals of Machine Learning and Artificial Intelligence and its applications to solve practical engineering problems by using Python language.
Understand the fundamentals of Linear Algebra with Python and cover the concepts of Anaconda, Pandas and Numpy.
Gain experience in solving practical problems like the Industrial Knowledge Representation using Decision Trees, Industrial Fault Diagnosis using Feedforward Neural Networks and much more.
Machine learning is undoubtedly one of the most exciting technologies in recent times. Both large and small companies are embracing it with tremendous results. If you are keen to harness machine learning technologies in your engineering work, become a better problem solver, or are perhaps considering a career in machine learning, then this program is for you.
There are many programs out there, but this one focuses on engineering and industrial applications.
The best way to learn the technologies is to work through practical examples of machine learning in a systematic way. Two types of machine learning will be tackled in this program: supervised learning and unsupervised learning.
Supervised learning is where we learn the relationship of given inputs to a set of outputs. For example, how different sensor inputs for a process plant can predict the likelihood of a breakdown of a pump or requirement for maintenance on an item of equipment. You already know how to classify the earlier input data to previous breakdowns of the pump, so, you want to use new input data to predict this event so that you can act before it actually happens. Algorithms that you will learn about here include linear regression, logistic regression, discriminant analysis, decision trees, Naïve Bayes, support vector machines, and random forests.
Unsupervised learning occurs when there is no pre-defined relationship between input data and an output variable. An example here would be to take sensor data from hundreds of similar industrial plants, then asking the algorithm to find patterns and classify the data. You want your algorithm to find any patterns and to do the classification of the data for you. Algorithms that you will learn about here include K-means clustering and Gaussian Mixture models.
Receive a Certificate of Completion from EIT.
Learn from well-known faculty and industry experts from around the globe.
Flexibility of attending anytime from anywhere, even when you are working full-time.
Interact with industry experts during the webinars and get the latest updates/announcements on the subject.
Experience a global learning with students from various backgrounds and experience which is a great networking opportunity.
Learn to use Python Programming to work with machine learning applications and to apply supervised and unsupervised machine learning to engineering problems.
Study the fundamentals of Machine Learning and Artificial Intelligence and its applications to solve practical engineering problems by using Python language.
Understand the fundamentals of Linear Algebra with Python and cover the concepts of Anaconda, Pandas and Numpy.
Gain experience in solving practical problems like the Industrial Knowledge Representation using Decision Trees, Industrial Fault Diagnosis using Feedforward Neural Networks and much more.
Machine learning is undoubtedly one of the most exciting technologies in recent times. Both large and small companies are embracing it with tremendous results. If you are keen to harness machine learning technologies in your engineering work, become a better problem solver, or are perhaps considering a career in machine learning, then this program is for you.
There are many programs out there, but this one focuses on engineering and industrial applications.
The best way to learn the technologies is to work through practical examples of machine learning in a systematic way. Two types of machine learning will be tackled in this program: supervised learning and unsupervised learning.
Supervised learning is where we learn the relationship of given inputs to a set of outputs. For example, how different sensor inputs for a process plant can predict the likelihood of a breakdown of a pump or requirement for maintenance on an item of equipment. You already know how to classify the earlier input data to previous breakdowns of the pump, so, you want to use new input data to predict this event so that you can act before it actually happens. Algorithms that you will learn about here include linear regression, logistic regression, discriminant analysis, decision trees, Naïve Bayes, support vector machines, and random forests.
Unsupervised learning occurs when there is no pre-defined relationship between input data and an output variable. An example here would be to take sensor data from hundreds of similar industrial plants, then asking the algorithm to find patterns and classify the data. You want your algorithm to find any patterns and to do the classification of the data for you. Algorithms that you will learn about here include K-means clustering and Gaussian Mixture models.
- 獎學金 - 查看所有獎學金
開始日期及學費
如何申請
評價與排名
學生評價
尚未收到該校的評價
關於Engineering Institute of Technology
近期活動
想了解更多關於此處的資訊?請看 IDP如何蒐集並展示課程資訊. 此處的資訊內容若有誤,IDP不負任何責任與義務。建議您直接洽詢IDP的專業顧問,以獲得最新最準確的資訊。
我們開始吧!
一鍵註冊或登入
檢視清單 或關閉此視窗以繼續搜尋