Book description
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 scikitlearn 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 pvalues 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
Publisher resources
Table of contents
 Preface
 1. Basic Math and Calculus Review
 2. Probability
 3. Descriptive and Inferential Statistics
 4. Linear Algebra

5. Linear Regression
 A Basic Linear Regression
 Residuals and Squared Errors
 Finding the Best Fit Line
 Overfitting and Variance
 Stochastic Gradient Descent
 The Correlation Coefficient
 Statistical Significance
 Coefficient of Determination
 Standard Error of the Estimate
 Prediction Intervals
 Train/Test Splits
 Multiple Linear Regression
 Conclusion
 Exercises

6. Logistic Regression and Classification
 Understanding Logistic Regression
 Performing a Logistic Regression
 Multivariable Logistic Regression
 Understanding the LogOdds
 RSquared
 PValues
 Train/Test Splits
 Confusion Matrices
 Bayes’ Theorem and Classification
 Receiver Operator Characteristics/Area Under Curve
 Class Imbalance
 Conclusion
 Exercises
 7. Neural Networks
 8. Career Advice and the Path Forward

A. Supplemental Topics
 Using LaTeX Rendering with SymPy
 Binomial Distribution from Scratch
 Beta Distribution from Scratch
 Deriving Bayes’ Theorem
 CDF and Inverse CDF from Scratch
 Use e to Predict Event Probability Over Time
 Hill Climbing and Linear Regression
 Hill Climbing and Logistic Regression
 A Brief Intro to Linear Programming
 MNIST Classifier Using scikitlearn
 B. Exercise Answers
 Index
 About the Author
Product information
 Title: Essential Math for Data Science
 Author(s):
 Release date: May 2022
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9781098102937
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