Essential math for data science : take control of your data with fundamental linear algebra, probability, and statistics
Nield, Thomas
Essential math for data science : take control of your data with fundamental linear algebra, probability, and statistics by Thomas Nield - Mumbai : Shroff Publishers, ©2022 - xiv, 332 p. : ill. ; 24 cm.
This book includes index.
1. Basic math and calculus review 2. Probability 3. Descriptive and inferential statistics 4. Linear algebra 5. Linear regression 6. Logistic regression and classification 7. Neural networks 8. Career advice and the path forward
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. 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: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks --
9781098102937
2023276388
GBC290257 bnb
020621576 Uk
Data mining--Mathematics.
Machine learning--Mathematics.
Mathematical statistics.
Probabilities.
Computer science--Mathematics.
Computer science--Mathematics.
Data mining--Mathematics.
Mathematical statistics.
Probabilities.
Handbooks and manuals.
Handbooks and manuals.
QA76.9.D343 / N54 2022
006.310 / NIE-E
Essential math for data science : take control of your data with fundamental linear algebra, probability, and statistics by Thomas Nield - Mumbai : Shroff Publishers, ©2022 - xiv, 332 p. : ill. ; 24 cm.
This book includes index.
1. Basic math and calculus review 2. Probability 3. Descriptive and inferential statistics 4. Linear algebra 5. Linear regression 6. Logistic regression and classification 7. Neural networks 8. Career advice and the path forward
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. 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: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks --
9781098102937
2023276388
GBC290257 bnb
020621576 Uk
Data mining--Mathematics.
Machine learning--Mathematics.
Mathematical statistics.
Probabilities.
Computer science--Mathematics.
Computer science--Mathematics.
Data mining--Mathematics.
Mathematical statistics.
Probabilities.
Handbooks and manuals.
Handbooks and manuals.
QA76.9.D343 / N54 2022
006.310 / NIE-E