Learning Data Science (seventh Early Release). O'reilly Media, Inc.

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure for data scientists to ensure data privacy. Unfortunately, integrating privacy into your data science workflow is still complicated. This essential guide will give you solid advice and best practices on breakthrough privacy-enhancing technologies such as encrypted learning and differential privacy–as well as a look at emerging technologies and techniques in the field.

The Theoretical and Practical Foundations of Machine Learning. Master Matrices, Linear Algebra, and Tensors in Python

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, clearly explaining how to apply the best practices from software engineering to data science.

Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!

Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!

Learn Mathematics and Statistics of Machine Learning, Artificial Intelligence, Neural Networks and Deep Learning

Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn and dive into machine learning A-Z with Python and Data Science.

Data Science & Machine Learning A-Z & Kaggle with Heart Attack Prediction projects and Machine Learning Python projects