Motivation
In the context of Machine Learning and Data Science Python and it’s rich ecosystem of libraries and development environments have evolved to be the number one programm language. The main advantages of Python are:
- It has a readable and intuitive syntax and is therefore easy to learn and apply. It is outstanding for rapid prototyping.
- Due due it’s interactive interpreter, Python code can easily be developed in an interactive manner, e.g. in Jupyter Notebooks. It is therefore ideal for scientific experimentations.
- Python supports object oriented programming as well as other programming paradigms such as procedural or functional programming.
- Python is cross-platform cabable, i.e. the programs run on Windows, MAC and Linux without requiring platform-specific adaptations
- Integration and Flexibility: Python plays well with other languages (C++, Java, R) and platforms (SQL databases, cloud services). It’s used in everything from web apps to embedded systems, making it ideal for end-to-end ML pipelines.
- Python has a massive community support
- Python has been early been adopted as numer-one programing language for Machine Learning and Data Exploration by major tech companies like Google, Meta, OpenAI, and Microsoft.
- Python provides an immense ecosysteme of libraries in particular for scientific computing, data science and machine learning. Some of the main libraries are
- NumPy: Fast numerical computations
- Matplotlib Data visualization
- Pandas Data manipulation and analysis
- Scikit-learn Classical machine learning algorithms
- TensorFlow Deep learning frameworks
- PyTorch Deep learning frameworks
- Transformers Access to Hugin Face Hub with a large bunch of pretrained models.
The libraries mentioned above are extremely valuable for developing machine learning and data mining applications. However, this Python precourse focuses on Python itself and does not introduce in external libraries.
Contents
In this course the basics of Python are introduced. The concrete contents are listed in the table below. For each of the items a video and a corresponding Jupyter Notebook is provided (see table below).
It is recommended that you first clone the entire repo Python Precourse. After watching a single video try to solve the tasks of the associated Jupyter notebook by yourself. Only then continue with watching the next video.
The study of this Python precourse is manadatory for attending all other AI related lectures provided by me (except for lecture Artificial Intelligence )