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About Essential Math for Data Science (Fifth Early Release)
<span><p>To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.</p><p>Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:</p><ul><li>Recognize the nuances and pitfalls of probability math</li><li>Master statistics and hypothesis testing (and avoid common pitfalls)</li><li>Discover practical applications of probability, statistics, calculus, and machine learning</li><li>Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added</li><li>Perform calculus derivatives and integrals completely from scratch in Python</li><li>Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks</li></ul></span>
Detailed Information
| Author: | Thomas Nield |
|---|---|
| Publication Year: | 2022 |
| ISBN: | 1098102924 |
| Pages: | 452 |
| Language: | other |
| File Size: | 10.0677 |
| Format: | |
| Price: | FREE |
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