# Introduction to Python

Python is an interpreted, object-oriented, high-level programming language, well known by engineers, researchers as well as hackers and also many private users. Pythons syntax as well as its sophisticated built in data types like lists, arrays and sets make it possible to write mighty code that is still compact and well readable. Because Python is also compleatly free and platform-indipendent many users can contribute new libraries and modules. This leads to a continuously growing area of applications like machine learning, data science, media processing and much more. There are also many tutorials and discussions regarding different topics, which makes it easy to learn Python even as a beginner.

# Related

- [notebook] Introduction - Python
- [notebook] Working with table data
- [notebook] Using pandas
- [notebook] Analyzing dependencies
- [notebook] Machine Learning
- [notebook] Autoencoder for image compression
- [notebook] Adaptive Boosting
- [notebook] RGB & HSV, color space transformations in Python
- [slides] Deep Learning With Point Clouds
- [notebook] KDE and KNN with Python
- [notebook] Logistic Regression with Python using Scikit-Learn
- [notebook] Python - Implementing a convolution
- [notebook] Image representation and processing in Python
- [notebook] Python - Jit Basics
- [notebook] Python - Matplotlib
- [notebook] Python - Numpy Basics
- [notebook] Python Tutorial Chapter 1
- [notebook] Python Tutorial Chapter 2
- [notebook] Python Tutorial Chapter 3
- [notebook] Python Tutorial Chapter 4
- [notebook] Python Tutorial Chapter 5
- [notebook] Python Tutorial Chapter 6
- [notebook] Python Tutorial Chapter 7
- [notebook] Python - Basics
- [notebook] Support Vector Machine with Python using Scikit-Learn
- [notebook] Decision Tree and Random Forest with Python
- [notebook] Clustering with the Expectation-Maximization algorithm
- [notebook] Face detection
- [notebook] Iterative Clostest Point (ICP)
- [] Python and C++
- [notebook] Clustering with the k-means algorithm
- [notebook] Linear regression with Python
- [notebook] 3D Points Docker environment
- [notebook] Mapreduce in Python - Word Count
- [slides] ML Deep Learning
- [slides] ML Programming Languages
- [notebook] Principal Component Analysis (PCA)
- [notebook] Point Cloud to Depth Image
- [notebook] Random Forest
- [notebook] Random Sample Consensus (RANSAC)
- [notebook] RANSAC Optimization & Depth Image to Point Cloud
- [notebook] Rotation of 3D Points
- [notebook] Similarity Transformation estimation