Andreas Koch has a background in data science and has a substantial experience with both analysing data and productionizing machine learning models. Currently, Andreas is working as a data science consultant advising organisations on how to best leverage their data.

Understand the concepts of Machine Learning
Machine Learning is not a new field but it has received a lot of attention in recent years as an important tool when it comes to handling big data and building the AI applications of the future. Machine Learning models are now being used to solve many different problems, from predicting when industrial machinery needs replacement to focusing cameras on mobile phones.

With machine learning it becomes possible to build systems that improves with more data, which is a fundamentally different approach compared to traditional rule-based programming. This course will introduce the concepts of machine learning to allow participants to recognize problems that are best approached with machine learning.

The course has a number of hands-on exercises that will allow participants to gain practical experience with training and evaluating machine learning models for a range of different types of problems.

Course agenda

The two-day course will be instructor led with hands-on exercises. The focus will be on giving the participants the knowledge and the confidence to apply machine learning to problems that they face in their own work. The course will touch upon many aspects of machine learning, but emphasis will be on classification tasks

Participants are expected to bring own laptop to the class, everything else needed for course is provided. The hands-on exercises will be browser-based, so there is no need to install software, but participants should either have or be willing to sign-up for a free Google account.

Day 1

• Concepts of Machine Learning
• Data preparation
• Logistic regression
• Overfitting
• Python, NumPy, Tensorflow
• Multilayer perceptron

Day 2

• Working with natural language
• Bag of words
• Deep Learning
• Image recognition
• Neural networks

Target audience
The course is well-suited for software developers or engineers wishing to add machine learning to their toolbox.

Participants should be interested in working with data and willing to learn how to extract value from data.

Prerequisites
Participants should be comfortable writing code and ideally have some familiarity with Python. It is not necessary to have any prior experience with machine learning and only basic mathematics are required.

After this course you will be able to:

• Recognize problems that are suitable for machine learning.
• Prepare data and train a classification model.
• Understand the differences between some of the most popular machine learning models.
• Evaluate how good a machine learning model is.
• Understand how machine learning can be applied to numerical, text and image data.

After this course, the organization will:

• Gain a competitive advantage by having employees with machine learning knowledge.
• Be able to prepare for the future by collecting data suitable for machine learning.