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Preface

How to Use This Book

An Executable Book
Guided Reading

Warm-Up

Data Science, AI, Machine Learning
Python and Jupyter
Computers and Programming

Python for Data Science

Crash Course
Variables and Operators
Lists and Friends
Strings
Accessing Data
- File IO
- Text Files
- ZIP Files
- CSV Files
- HTML Files
- XML Files
- Web Access
Functions
Modules and Packages
Error Handling and Debugging Overview
Inheritance
Further Python Features

Managing Data with Python

Efficient Computations with NumPy
Saving and Loading Non-Standard Data
High-Level Data Management with Pandas

Data Visualization

Matplotlib
Plotly
Folium

Supervised Learning

Machine Learning Overview
General Considerations
Linear Regression
Logistic Regression
Artificial Neural Networks
Decision Trees
Ensemble Methods
- The Idea
- Stacking
- Bagging
- Boosting
Support-Vector Machines
Naive Bayes Classification
Text Classification
- Preprocessing Text Data
- Training

Unsupervised Learning

Typical Tasks
Quality Measures for Clustering
Centroid-based Clustering (k-Means)
Hierarchical Clustering
Density-based Clustering
- DBSCAN
- OPTICS
Distribution-based Clustering
Autoencoders
Nonlinear Dimensionality Reduction Overview
Kernel PCA
Multidimensional Scaling
Locally Linear Embedding
Stochastic Neighbor Embedding
Self-Organizing Maps

Reinforcement Learning

Overview and Examples
Stateless Learning Tasks (Multi-armed Bandits)
Markov Decision Processes
- Basic Notions
- Bellman Equations and Optimal Policies
Dynamic Programming
Monte Carlo Methods
Temporal Difference Learning (TD Learning)
Approximate Value Function Methods
Policy Gradient Methods

Exercises

Computer Basics
Python Programming
Managing Data
Data Visualization
- Matplotlib Basics
- Advanced Matplotlib
Reinforcement Learning
- Modeling
- Markov Decision Processes

Projects

Install and Use Python
Python Programming
Weather
MNIST Character Recognition
Cafeteria
Public Transport
Corona Deaths
Chemnitz Trees
Forged Banknotes
House Prices
Hyperparameter Optimization for Cats and Dogs
Blogs
- Blog Author Classification (Training)
- Blog Author Classification (Test)
Supermarket Customers
Chinese Celadons
- Hierarchical Clustering
- Density-based Clustering
Color Perception
Forest Fires
Online Advertising
Frozen Lake
Q-Learning for Tic-Tac-Toe
Cart Pole

Mathematics

Logic
Combinatorics
Linear Algebra

Computer Science

Unified Modeling Language (UML)
Cloud Computing

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Chinese Celadons

Chinese Celadons#

This series of projects investigates the relations between different types of Chinese celadons (early porcelains).

Hierarchical Clustering
Density-based Clustering

previous

Supermarket Customers

next

Hierarchical Clustering

By Jens Flemming

Website of the author with information on current data science lecture series
Zwickau Data Science Course provides information for prospective students

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