Plus Plus Pty Ltd

This course covers specific Python techniques for scientific programming, numerical analysis and data analytics.

Upon successful completion of this course you will be able to:

• Installing tools and modules
• Set up and use IPython interactive shell and use Jupyter/IPython Notebook.
• Understand core Python issues and traps with numbers.
• Understand and use core Python numerical modules.
• Use numpy.
• Use scipy, including:
  • image processing with the scikits - scikit-image
  • essential machine learning with scikit-learn. Supervised learning, classification, regression.
  • Unsupervised learning. An overview of TensorFlow, Pythorch and others.
  • Understanding LLMs. Accessing an LLM. Building an LLM.
• Create all kinds of charts with matplotlib.
• Use pandas for data analysis.
• Use other libraries, including SymPy for mathematical expressions and RPy for R programmers.
• Extend Python with Cython (15 times faster!)
• Extend Python with C, C++ (30 times faster!)

This course is aimed at experienced Python programmers who have completed our Intermediate Python course or (preferably) our Advanced Python course.
(Or contact us to see how we could fit it into a tailored course.)

Substantial recent experience with Python as covered in our Intermediate Python course or (preferably) Advanced Python course is required.

Introduction to Python (2 days)
Intermediate Python (3 days)
Advanced Python (3 days)