The Ultimate Python Course for Data Science, ML & AI
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Data science refers to the extraction of knowledge from unstructured and structured data using statistical methods, algorithms and artificial intelligence.
This course is intended to enable participants to confidently implement data science applications with the Python programming language and to independently use established methods of machine learning, especially deep learning. For this purpose, the course is divided into four building blocks:
- Python basics
- Data Science
- Machine Learning
- Deep Learning / Neural Networks
No prior knowledge of these topics is necessary for participants to successfully complete the course: at the beginning of the course, all participants are brought to the same level.
In the first block, the easy-to-understand, performant and powerful programming language Python is introduced, which provides the foundation for later data evaluations. By the end of this topic block, participants are already able to write small programs such as a spam filter.
Subsequently, the Data Science block deals with various methods of data analysis that are used by institutions to derive recommendations for action or optimize the quality of products, for example. Participants learn here to read in, filter and graphically evaluate data with Python. For this purpose, the tools Numpy, Pandas, Matplotlib and Seaborn are introduced and their use is practiced.
In the next block, course participants learn about different types and methods of machine learning and learn how they can use them to extract patterns and regularities from datasets. Participants are taught the Scikit-learn tool for this purpose. With it, data is prepared to train models, which are then evaluated and optimized.
Then participants have acquired the prior knowledge to be able to get into the topic of Deep Learning / Neural Networks in the last block: Deep Learning is a special method of machine learning and the associated information processing, which is based on the principle of neural networks. Starting with the functioning of a neuron, participants are guided to design and train multi-layer neural networks. With the tools Keras and Tensorflow, automatic image recognition is produced here.
Comprehensive practical examples, quizzes, exercises, tests and cheat sheets support participants in a deep understanding of the content and ensure that they can apply their new knowledge beyond the course.