Introduction to Machine Learning
Gain more value from your data by adding machine learning to your toolboxDel siden Print
Understanding the concepts of Machine Learning
In recent years, machine learning has received increasing attention as a tool for handling the big data building AI applications of the future. While machine learning is not a new field, it is now being used to solve several different problems from predicting replacement of industrial machinery to focusing cameras on mobile phones.
Machine learning makes it possible to build systems that improves with more data, which is an essentially different approach compared to the traditional rule-based programming. In this course, you will be introduced to the main concepts, so you will be able to recognize problems that can be approached with machine learning.
Participants will take part in a variety of hands-on exercises, that will help gain practical experience with both training and evaluation machine learning models for different types of problems.
Who can participate?
This course is particularly suited for software developers or engineers, who wish to expand their toolbox with machine learning.
Participants should have interest in working with data and be willing to learn how you can extract value from data.
Participants should have experience with and feel comfortable writing code. Ideally, participants will have some familiarity with Python.
Participation does not require any prior experience with machine learning, and only basic mathematics are needed for the course.
Benefits for you
- Recognizing problems that are suitable for machine learning
- Preparation of data and training a classification model
- Understanding the differences between the most popular machine learning models
- Evaluation of how good and applicable a machine learning model is
- General understanding of how machine learning can be applied to numerical, text and image data
Benefits for your company
- A competitive advantage by having employees with machine learning knowledge
- Ability to prepare for the future by collecting data suitable for machine learning
The course runs over two full days.
The course focuses on providing participants with not only the necessary knowledge, but also the confidence to go and apply machine learning to concrete problems that they face in their own work. The instructor therefore provides hands-on exercises to ensure your practical outcome of the course.
The course deals with several aspects of machine learning, but there will be an emphasis on classification tasks.
Participants are only expected to bring their own laptop, as everything else will be provided. Additionally, participants should already have a free Google account or be willing to sign-up, but there is no need to install software, as the hands-on exercises are browser-based.
- The concepts of Machine Learning
- Preparation of data
- Python, NumPy, Tensorflow
- Multilayer perceptron
- Logistic regression
- Deep Learning
- Image recognition
- Neural networks
- How to work with natural language
- Bag of words
Andreas Koch is the instructor on the course and with a background in data science, he has substantial and relevant experience with analysing data as well as productionizing machine learning models.
Andreas currently works as a data science consultant, where he advises organisations on how to best leverage and benefit from their data.
The course and course material will be conducted in English.
Mannaz works in close collaboration with IDA, The Danish Society of Engineers.
When you sign up for this course, Mannaz handles your registration, while IDA manages the course execution.
Hvis du ønsker at komme på venteliste så udfyld felterne herunder. Vi kontakter dig hvis der kommer en ledig plads på kurset.