Math for Data Science presents the mathematics necessary for Data Science. The book is suitable for courses in applied mathematics, business analytics, computer science, data science, and engineering.
The text covers the portions of linear algebra, calculus, probability, and statistics prerequisite to Data Science, and includes a treatment of machine learning.
Clear examples are supported with detailed figures and Python code, and Jupyter notebooks and supporting files are available on this website. More than 380 exercises are provided to aid understanding, as well as nine detailed appendices covering background elementary material.
The book begins at a gentle pace, by focusing on two-dimensional datasets. As the text progresses, foundational topics are expanded upon, leading to deeper results at a more advanced level.
The highlight of the book is the machine learning chapter, where the results of the previous chapters are applied to neural network training and stochastic gradient descent. Also included in this last chapter are advanced topics such as accelerated gradient descent and logistic regression trainability.
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