Essential math for data science: take control of your data with fundamental linear algebra, probability, and statistics
Publication details: Mumbai: O'Reilly Media & Shroff Publishers & Distributors, 2023.Description: xiv, 332p.: ill.; pbk.: 23cmISBN:- 9789355422743
- 006.310151 NIE
Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|
Books | IIT Gandhinagar | General | 006.310151 NIE (Browse shelf(Opens below)) | 1 | Available | 033737 |
Includes Supplement topics, Exercise answer
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.
Learn how to:
Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning
Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon
Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance
Manipulate vectors and matrices and perform matrix decomposition
Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks
Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
https://www.oreilly.com/library/view/essential-math-for/9781098102920/
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