Many organizations have to deal with more and more data. Machine learning is gaining attention as a tool for extracting value from all this data. This course is an introduction to the concepts and applications of machine learning.
Introduction to Machine Learning
Data is at the core of all business decisions. Machine learning is gaining attention as a tool for extracting the value from data and making improved decisions that keep a company ahead of the competition.
In this course you’ll be introduced to the concepts and applications of Machine Learning. You will learn various supervised and unsupervised ML algorithms and prediction tasks applied to different data. Moreover, by taking this course you’ll be able to understand how to choose the right model for your data.
Machine Learning Concepts
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.Are you working with large data sets? Then this Machine Learning course is for you
This introductory course is aimed at anyone curious to know more about Machine Learning and looking for an understanding of simple yet effective Machine Learning tools.
Your background is not crucial but most likely you are already working with large amounts of data today and want to learn how to get the best value out of these.
Basic coding or scripting knowledge is required.
After this course you will be able to:
On completing the course, you will be able to:
- Recognize different business problems that could be addressed using the potential of machine learning and artificial intelligence.
- Identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes.
- Understand how machine learning can be applied to numerical, text and image data.
- Clean and prepare data, perform Exploratory Data Analysis (EDA) and train classification models.
- Identify the differences between some of the most popular machine learning models.
- Evaluate how good a your machine learning model is and how to incorporate best practices.
After this course, the organization will:
Gain a competitive advantage by having employees with machine learning knowledge. And will be able to prepare for the future by collecting data suitable for machine learning
Course agenda on Machine Learning
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 09:00-16:00
Introduction to Machine Learning
Supervised VS Unsupervised Learning
Python, NumPy, Tensorflow
Day 2 09:00-16:00
Working with natural language
Bag of words
Training Neural networks
Tips and tricks on how to come forward from here
Muniba Talha is associate professor at Copenhagen School of Design and Technology (KEA) and external lecturer Copenhagen Business School (CBS). Prior to this position Muniba worked at Project and Team lead positions delivering robust software products and developing agile teams.
Muniba is passionate about lifelong learning, community building, diversity, and inclusion. She is founder of Women in Data Science and Machine Learning, a community open to everyone interested in this area and to support and educate the women in the field of data science and machine learning.