The training "Machine Learning with Python and Scikit-learn" leads you to the fundamentals of Machine Learning in 5 days. It starts with the syntax, data types, and philosophy of Python. You will learn why the language is so well suited for Data Science.
In the Python part, you work with lists, tuples, dictionaries, strings, sets, and FrozenSets and organize your code into functions and modules. For data visualization, you use Matplotlib and get to know Seaborn as an alternative. Jupyter Notebooks serve as your working environment.
In the Machine Learning part, you get an introduction to AI terminology and the methods of classification and regression. With Scikit-learn, you practically implement the k-nearest Neighbor Classifier, Decision Trees, and Random Forests. In addition, you learn the basics of neural networks in Python.
A separate topic block covers anomaly detection, including with Isolation Forests and the cluster analysis DBSCAN. This way, you can create models for classification, prediction, and anomaly detection yourself at the end.
The training is aimed at developers, data scientists, and analysts. Knowledge of a programming language is required. A balanced mix of theory and practice is taught, with direct exchange with the trainers and other participants.
You can attend the course either in person in Nuremberg or online in the virtual classroom. At the end, you will receive a certificate of participation as well as the digital IT-Schulungen Open Badge as additional proof of competence, which you can share on LinkedIn, for example.
