Essential Math for Data Science
Thomas Nield founded Nield Consulting Group and teaches at O'Reilly Media and the University of Southern California. He enjoys making technical stuff understandable and relevant to those who are unfamiliar with it or are scared by it. Thomas gives classes on data analysis, machine learning, mathematical optimization, and practical artificial intelligence on a regular basis.
Learn the arithmetic required for success in data science, machine learning, and statistics. Author Thomas Nield walks you through disciplines such as calculus, probability, linear algebra, and statistics and how they apply to techniques such as linear regression, logistic regression, and neural networks in Essential Math for Data Science. You'll also acquire practical insights into the current status of data science and how to apply those insights to advance your career.
Discover how to:
- Explore important mathematical concepts such as calculus, linear algebra, statistics, and machine learning using Python code and libraries like as SymPy, NumPy, and scikit-learn.
- Understand linear regression, logistic regression, and neural networks in plain English, with little mathematical notation or jargon.
- To interpret p-values and statistical significance, use descriptive statistics and hypothesis testing on a dataset.
- Vector and matrix manipulation, as well as matrix decomposition
- Apply incremental understanding of calculus, probability, statistics, and linear algebra to regression models, including neural networks.
- Navigate a data science career practically, avoiding typical mistakes, assumptions, and prejudices while honing your skill set to stand out in the job market.
Author: Thomas Nield
Link to buy: https://www.amazon.com/Essential-Math-Data-Science-Fundamental/dp/1098102932/
Best Sellers Rank: #24,551 in Books
#5 in Linear Algebra (Books)
#8 in Statistics (Books)
#10 in Calculus (Books)
