This workshop is for you if you are keen to get your own Machine Learning journey started. You don’t need convincing that Machine Learning is here to stay and you are excited to try it out yourself. You might feel overwhelmed with the amount of information available online. You might be unsure about how to get started. Or you have already tried a few things yourself and are now looking for a comprehensive, practical introduction that combines the best of what others have already figured out. It doesn’t really matter what your background is – you could be from any department involved in some sort of analytical work, e.g. as a business analyst, junior data scientist, IT engineer, or a manager. If any of this sounds like you, don’t hesitate to join in!
Machine Learning has quickly become a crucial success factor for digitization across all industries. This workshop helps you get started by introducing key concepts, application areas, and methods of Machine Learning.
The Kickstarter covers both an introduction to Machine Learning as well as an examplary workflow from data exploration and preparation through feature engineering to model training and validation. Practical excercises using Python’s open-source ecosystem continually support and illustrate the methodological content.
The topics include a disambiguation of core terminology, data preparation and feature engineering for machine learning, an overview of common machine learning algorithms and the application of linear models and tree-based ensembles to classification and regression problems. The workshop concludes with an introduction into using Deep Learning for image classification.
After completing the workshop, participants are familiar with modern Supervised Machine Learning approaches and know how to approach a Machine Learning project.
Our experienced instructors have several years of professional experience in various industries and are focused on providing you with highly practical information that you can directly use in your own work.
An understanding of key Machine Learning concepts and the standard ML workflow
Practical experience and a high-level methodological familiarity with modern Supervised Machine Learning methods
A Kickstart and pointers to start using Machine Learning in your daily work
Basic knowledge in statistics / mathematics and familiarity with a programming language helps, but anyone can get started with ML. A laptop is required for the practical exercises.
- Location: meetinn Munich – Obersendling
- Language: English
- max. participants: 15
- Time: 10:00 a.m. – 5:30 p.m.
Are you interested in this workshop?
- Introduction to Machine Learning
- Overview and motivation: Why is Machine Learning important?
- Disambiguation of core concepts and terminology
- Data Science vs. ML vs. AI
- Supervised vs. Unsupervised vs. Reinforcement Learning
- Classification vs. Regression vs. Clustering
- The Machine Learning Workflow
- The big picture: Machine Learning in the broader context of data projects
- A typical workflow
- Data Exploration and Preparation
- Exploratory data analysis
- Data cleaning and transformation
- Machine Learning Model Development
- Feature engineering
- Introduction to common Machine Learning algorithms
- Linear Models
- Tree-based Ensembles
- Neural Networks
- Model evaluation and interpretation
- Outlook: Introduction to Deep Learning for image classification