Resources (data science)

Towards data science: Gratis resources

Hieronder de boeken die genoemd worden in dit artikel.

Ik ben nieuw op dit gebied. Vermoedelijk voor mij op dit moment de meest relevante boeken:

1. Python Data Science Handbook - Want practisch + O'Reilly
2. Bayesian Methods for Hackers - Heb ik me altijd al in willen verdiepen
3. D2L
4. Deep Learning.

Deep Learning

• https://www.deeplearning.ai/machine-learning-yearning
• Alleen beschikbaar in HTML-format. Ook beschikbaar als gewoon boek (waar je uiteraard voor moet betalen)
• Veld: deep learning
• This is not a book full of code and corresponding comments, or a surface-level hand-wavy overview of neural networks. This is an in-depth mathematics-based explanation of the field. [1]

Dive into Deep Learning (D2L)

• https://d2l.ai/chapter_preface/index.html
• interactive Deep Learning book with code, math, and discussions. It provides NumPy/MXNet, PyTorch, and TensorFlow implementations

Machine Learning Yearning

• https://www.deeplearning.ai/machine-learning-yearning/ - Je moet je persoonlijke gegevens achterlaten om toegang te krijgen

Interpretable Machine Learning

• https://christophm.github.io/interpretable-ml-book
• Interpretable Machine Learning focuses on ML models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks.

Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable. It details how to select and apply the best interpretation methods for a machine learning project.

Bayesian Methods for Hackers

• https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

[2]:

Bayesian Methods for Hackers is not technically a Machine Learning book as it focuses on an important field of Data Science called Bayesian inference.

Bayesian Methods for Hackers is designed as an introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. It is aimed at enthusiast with a less mathematical background or one who is not interested in mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.

This book is also a great resource to learn PyMC, the probabilistic programming language in Python.

Python Data Science Handbook

• https://jakevdp.github.io/PythonDataScienceHandbook
• O'Reilly

Python Data Science Handbook is aimed at junior Data Scientists. It shows how to use the most important tools, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and many others. This book is perfect for tackling day-to-day issues such as cleaning, manipulating, and transforming data — or building machine learning models.

An Introduction to Statistical Learning

• http://faculty.marshall.usc.edu/gareth-james/ISL
• With applications in R

[3]:

An Introduction to Statistical Learning provides an introduction to statistical learning methods. It is aimed at upper-level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real-life settings and should be a valuable resource for a practicing data scientist.

Introductions to data science - YouTube

Artificial Intelligence Tutorial using Python | Edureka

• Artificial Intelligence with Python | Artificial Intelligence Tutorial using Python | Edureka

Other

A Whirlwind Tour of the Python Language

• https://github.com/jakevdp/WhirlwindTourOfPython
• Zusterproject van Python Data Science Handbook - Misschien handig voor mij als standaard-resource om Python beter te leren

Keywords

• Artificial intelligence - Kunstmatige intelligentie
• Deep learning
• Machine learning