Fast data science: practical introduction to Python, machine learning methods and Statistics

Python Basics

  • 1. Introduction
  • 2. Getting started
  • 3. Basic data types
  • 4. Functions
  • 5. Conditionals and comparisons
  • 6. Loops
  • 7. Object oriented programming
  • 8. Modules and packages
  • 9. Some additional topics
  • 10. Debugging code

Machine learning tools

  • 1. Numpy
  • 2. Polars
  • 3. Scikit-learn
  • 4. Optuna
  • 5. Matploblib
  • 6. Pandas
  • 7. PyTorch
  • 8. PyTorch Lightning

Examples of applications

  • 1. Fraud detection
  • 2. Time series analysis
  • 3. Image classification with neural networks
  • 4. Text classification
  • 5. Demographic data analysis

Other

  • 1. Local linear neural networks
  • 2. Boostrapping and permutation tests
  • 3. Bias/variance tradeoff, mse, convergence, etc
  • 4. GAN
  • 5. Bayesian inference, Pyro, PyStan and VAEs
  • 6. Rpy2
  • 7. Simulation studies using sstudy
Fast data science: practical introduction to Python, machine learning methods and Statistics
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© Copyright 2017-2021, Marco Inacio. Licensed under GNU GPL 3.

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