Fall 2023 Python Programming for Data Science
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Lecture 1 - Short History of AI

  • Lecture 1 - A Short History and Current State of Artificial Intelligence

Theme 1 - Python Programming

  • Lecture 2 - Data Types in Python
  • Lecture 3 - Statements, Files
  • Lecture 4 - Functions, Iterators, Generators
  • Lecture 5 - Object-Oriented Programming
  • Lecture 6 - Exception Coding, Modules and Packages

Theme 2 - Data Engineering Pipelines

  • Lecture 7 - NumPy for Array Operations
  • Lecture 8 - Data Manipulation with pandas
  • Lecture 9 - Data Visualization with Matplotlib
  • Lecture 10 - Databases and SQL
  • Lecture 11 - Data Exploration and Preprocessing
  • Lecture 12 - Data Visualization with Seaborn

Theme 3 - Model Engineering Pipelines

  • Lecture 13 - Scikit-Learn Library for Data Science
  • Lecture 14 - Ensemble Methods
  • Lecture 15 - Artificial Neural Networks
  • Lecture 16 - Convolutional Neural Networks
  • Lecture 17 - Model Selection, Hyperparameter Tuning
  • Lecture 18 - Neural Networks with PyTorch
  • Lecture 19 - Natural Language Processing
  • Lecture 20 - Transformer Networks
  • Lecture 21 - NLP with Hugging Face
  • Lecture 22 - Diffusion Models for Text-to-Image Generation
  • Lecture 23 - Large Language Models

Theme 4 - Model Deployment Pipelines

  • Lecture 24 - Introduction to Data Science Operations (DSOps)
  • Lecture 25 - Deploying Projects as Web Applications
  • Lecture 26 - Deploying Projects to the Cloud
  • Lecture 27 - Reproducible Data Science Projects

Tutorials

  • Tutorial 1 - Jupyter Notebooks
  • Tutorial 2 - Terminal and Command Line
  • Tutorial 3 - Python IDEs, VS Code
  • Tutorial 4 - Virtual Environments
  • Tutorial 5 - Web Scraping
  • Tutorial 6 - Google Colab
  • Tutorial 7- Image Processing with Python
  • Tutorial 8 - TensorFlow
  • Tutorial 9 - PyTorch
  • Tutorial 10 - Tensorflow Datasets
  • Tutorial 11 - CometML
  • Tutorial 12 - GitHub
Fall 2023 Python Programming for Data Science
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© Copyright 2023, Alex Vakanski. Revision 9ee54043.

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