Data Science Books that people should read. Currently my personal reading list.
- Data Science from Scratch: First Principles with Python.
- Author(s): Joel Grus
- Publisher: O'Reilly Media, Year: 2015
- Python basics. Linear algebra, statistics and probability. Principles of Data collection and Exploratory data analysis. Basic Machine Learning algorithms.
- Python for Data Analysis
- Author(s): Wes McKinney
- Publisher(s): O'Reilly Media, Inc.
- Manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications.
- Introduction to Machine Learning with Python
- Author(s): Andreas C. Müller, Sarah Guido
- Publisher(s): O'Reilly Media, Inc.
- The steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Recommend for people that are completly new to Data Science and Machine Learning. Fundamental concepts. Advanced methods for model evaluation and parameter tuning. Data process and concept of pipelines and workflows.
- Hands-On Machine Learning with Scikit-Learn, Keras, & TensorFlow
- Author(s): Aurélien Géron
- Publisher(s): O'Reilly Media, Inc.
- You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
- Mathematics for Machine Learning
- Author(s): Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
- Publisher(s): Cambridge University Press.
- A book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, the book provides the necessary mathematical skills to read those other books.