Machine Learning Basic Course

The course covers a selection of the following topics:

• Types of Machine Learning
• The machine learning process (for supervised learning)
• Linear and logistic regression
• Preparation of data
• Visualization of data
• Decision trees, random forests, gradient boosted trees
• Designing a training process
• Finding errors (What to do if the training does not converge?)
• Unsupervised Learning

This course is designed for people who already have a basic knowledge of Python. You should be familiar with basic data types and control structures and be able to write simple programs so that you can focus on machine learning techniques.

If you don't have any programming experience yet, our Python Basic course is the right one for you.

Our practical course Machine Learning teaches you the basics of Machine Learning in a simple and structured way and many concrete techniques that you can apply in your projects. We start with an introduction to the different types of Machine Learning and the Machine Learning process. Then you will learn the main traditional Machine Learning techniques and see how you can apply them to different types of data. Along the way, you'll also practice using the frameworks that are most important for real-world applications, such as Scikit-Learn or XGBoost. You will learn how to prepare data for machine learning tasks and how to interpret and visualize the results of machine learning models.

By the end of the course, you will be able to select the right machine learning techniques for your projects and successfully apply these techniques to your data.

On the first day we deal with the basics of Machine Learning and get to know the most important Machine Learning techniques. We start with an introduction to the different types of Machine Learning and the Machine Learning process. We discuss the supervised learning approach and look at the basic techniques of linear regression and logistic regression. You will learn how to apply these techniques to your data through several examples.

Before any ML technique can be applied to a problem, it is necessary to analyze, clean, visualize and transform the existing data. On the second day, we will deal with this less glamorous but absolute topic of data preparation and use the Pandas library to cover many concrete examples. You'll learn how to load data in different formats, why it's important to prepare data for use with machine learning techniques, and how to concretely make that happen for different types of data. We also look at how to visualize data to get a better understanding of it and how to interpret the results of machine learning models.

On the third day, we go into some powerful techniques that you can use to solve a variety of machine learning problems. We start with decision trees and see what advantages but also what weaknesses this technique has. Next, you'll learn about Random Forests and Gradient Boosted Trees, which largely address the weaknesses of decision trees. You will learn how to apply these techniques to your data and how to use them to achieve results with high accuracy. This will provide you with enormously powerful tools that have not only won numerous machine learning competitions, but have also been used successfully in countless practical applications.

If we still have time, we will also briefly cover the topic of Unsupervised Learning, which you can use to find clusters in your data, for example.

Of course, as a participant in this course, you will receive a certificate. The prerequisite for this is complete participation in all course units and programming tasks.

3-day on-site course

The course takes place in our modern and top-equipped training rooms. We provide each participant with a modern laptop during the training. Drinks are provided by the Coding Academy. Access to the material will be provided at the latest on the first day of the course

3-day online course
The course takes place online. You only need a computer with Internet access; however, for some courses it is necessary to install the software used; this is specified in the description of the respective course. Access to the material will be provided at the latest on the first day of the course.

4-week program

A new and innovative learning concept. More information about the 4 weeks program can be found here.