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
Index
Index